Ensuring Reliability: A Comprehensive Guide to HPLC Method Robustness Testing in Food Analysis

Abstract

This article provides a detailed framework for understanding, implementing, and validating robustness testing for High-Performance Liquid Chromatography (HPLC) methods in food applications. Tailored for researchers and analytical scientists, it covers foundational principles, practical methodological approaches for complex food matrices, systematic troubleshooting, and alignment with regulatory validation standards. By integrating current guidelines, experimental design strategies, and real-world case studies from aflatoxin and nutraceutical analysis, this guide aims to equip professionals with the knowledge to develop reliable, transferable, and compliant analytical methods that ensure food safety and quality.

The Pillars of Reliability: Understanding HPLC Robustness and Its Critical Role in Food Safety

In pharmaceutical analysis and food safety research, the reliability of an analytical method is paramount. Robustness and ruggedness are two critical validation parameters that assess a method's resilience to variations, yet they are frequently confused or used interchangeably. Within the context of High-Performance Liquid Chromatography (HPLC) method development for food applications, distinguishing these terms is not merely semantic; it is fundamental to ensuring data integrity, regulatory compliance, and successful method transfer across laboratories.

This article provides analytical scientists with a clear, actionable framework to define, test, and apply the concepts of robustness and ruggedness. We will clarify that robustness measures a method's stability under small, deliberate variations in its internal procedural parameters, while ruggedness evaluates its reproducibility across external, real-world conditions such as different analysts, instruments, or laboratories [1] [2]. By embedding these assessments into the HPLC method validation workflow for food analysis, researchers can build more dependable and transferable methods, ultimately advancing the rigor of food safety and quality research.

Terminological Foundations: Internal vs. External Stability

Defining the Core Concepts

The distinction between robustness and ruggedness hinges on the source and type of variation introduced to the analytical method.

• Robustness is an assessment of a method's internal resilience. It is defined as "the ability of an analytical method to remain unaffected by small, but deliberate variations in method parameters" [3] [4]. This testing is performed during method development to identify which operational parameters are most sensitive and to establish permissible tolerances for them. For an HPLC method, these are the parameters explicitly written into the procedure [3].

• Ruggedness, in contrast, evaluates a method's external reproducibility. It is "the degree of reproducibility of test results obtained by the analysis of the same samples under a variety of normal, but different, test conditions" [3]. It measures the method's performance against the variations that occur naturally in a multi-user, multi-instrument environment, even when the written procedure is followed exactly [1] [2].

The following table synthesizes the key differences between robustness and ruggedness to provide a clear, at-a-glance comparison for scientists.

Table 1: Key Differences Between Robustness and Ruggedness in Analytical Method Validation

Aspect	Robustness	Ruggedness
Core Focus	Stability under small variations in method parameters [1]	Consistency across different operators, environments, and equipment [1]
Nature of Variations	Small, deliberate, and controlled (internal) [2]	Larger, environmental, and operational (external) [2]
Typical Test Variables	pH, mobile phase composition, flow rate, column temperature, wavelength [1] [3]	Different analysts, different instruments, different laboratories, different days [1] [3]
Primary Objective	To identify critical method parameters and set system suitability limits [4]	To ensure method reproducibility and reliability during routine use and transfer [1]
Testing Scope	Intra-laboratory (conducted during method development) [2]	Inter-laboratory or within-laboratory over time (assessed later in validation) [2]
Regulatory Context	Not formally required by ICH but highly recommended; investigated during development [3]	Addressed under "Intermediate Precision" and "Reproducibility" in ICH guidelines [3]

Experimental Protocols for Assessing Robustness and Ruggedness

A Practical Protocol for Robustness Testing in HPLC

Robustness testing should be initiated after the HPLC method is optimized but before full validation begins. The goal is to stress the method with minor parameter changes to define its operational limits [4].

1. Factor Selection and Level Definition:

• Select Critical Factors: Based on the HPLC method, choose factors likely to impact performance. Common factors include:
  • Mobile phase pH (e.g., ±0.1 units)
  • Mobile phase composition (e.g., organic modifier concentration ±1-2%)
  • Flow rate (e.g., ±0.1 mL/min)
  • Column temperature (e.g., ±2°C)
  • Detection wavelength (e.g., ±2 nm for UV/Vis detectors) [3] [4]
• Define Ranges: Set high (+) and low (-) levels for each factor that represent slightly exaggerated but realistic variations from the nominal method value.

2. Experimental Design: A systematic approach using multivariate experimental designs is highly efficient compared to the one-variable-at-a-time approach.

• Plackett-Burman Designs: Ideal for screening a large number of factors (e.g., 5-11) with a minimal number of experimental runs (e.g., 12 runs for up to 11 factors). These designs efficiently identify which factors have significant main effects [3] [4].
• Full or Fractional Factorial Designs: Suitable for a smaller number of factors (e.g., 2-5). A full factorial design tests all possible combinations of factor levels (2^k runs), while fractional factorial designs test a carefully chosen subset, saving time and resources while still revealing main effects and some interactions [3].

3. Execution and Data Analysis:

• Perform Experiments: Execute the experimental runs in a randomized order to minimize the influence of external bias.
• Measure Responses: For each run, record critical chromatographic responses such as retention time, peak area, resolution between critical peak pairs, tailing factor, and theoretical plates [4].
• Calculate Effects: For each factor, calculate its effect on the responses using the formula: Effect (X) = [ΣY(+)/N] - [ΣY(-)/N] where ΣY(+) and ΣY(-) are the sums of the responses when the factor is at its high or low level, respectively, and N is the number of runs at each level [4].
• Statistical and Graphical Analysis: Use statistical methods (e.g., t-tests) or graphical tools (e.g., Pareto charts, normal probability plots) to identify which factors have statistically significant effects on the method's performance.

4. Establishing System Suitability Limits: The results of the robustness test provide an experimental basis for setting system suitability test (SST) limits. For instance, if a small change in pH significantly impacts resolution, the SST should include a stringent resolution criterion to ensure the method functions correctly [4].

A Practical Protocol for Ruggedness Testing

Ruggedness testing demonstrates that the method produces reproducible results when the same method is applied under different, realistic conditions.

1. Define the Scope of Variables: Select the external variables to be studied. The most common are:

• Inter-analyst: Two or more qualified analysts perform the analysis using the same instrument, reagents, and protocol.
• Inter-instrument: The analysis is performed on different HPLC systems of the same model and configuration.
• Inter-laboratory: The method is transferred to a different laboratory for analysis.
• Day-to-day: The analysis is repeated on different days to account for environmental fluctuations and reagent preparation differences [1] [2].

2. Experimental Execution:

• Sample Preparation: Use a homogeneous and stable sample, preferably a validated reference material or a well-characterized in-house standard.
• Study Design: A nested or factorial design can be used. A typical approach is to have two analysts each perform the analysis in triplicate on two different instruments over two different days.
• Measurement: All participants or conditions must follow the identical, written analytical procedure.

3. Data Analysis and Acceptance Criteria:

• Statistical Analysis: Calculate the Relative Standard Deviation (RSD) for the results obtained under the varied conditions. This RSD is a measure of the method's intermediate precision, a key component of ruggedness.
• Acceptance Criteria: The method is considered rugged if the RSD for the quantitative result (e.g., assay content) meets pre-defined acceptance criteria, which are often based on the AOAC standard (e.g., RSD ≤ 8% for certain analyses) [5]. The results from different conditions should also be statistically comparable using analysis of variance (ANOVA).

Visualizing the Workflows

The following diagrams illustrate the logical flow for evaluating both robustness and ruggedness, providing a clear roadmap for scientists.

Robustness Testing Workflow

Ruggedness Testing Workflow

The Scientist's Toolkit: Essential Reagents and Materials

Successful validation of robustness and ruggedness requires high-quality, consistent materials. The following table details key reagents and their functions in the context of HPLC method validation for food analysis.

Table 2: Essential Research Reagent Solutions for HPLC Method Validation

Reagent/Material	Function in Validation	Key Considerations
HPLC-Grade Solvents (e.g., Methanol, Acetonitrile)	Mobile phase components for chromatographic separation.	Low UV absorbance, high purity to minimize baseline noise and ghost peaks. Consistency between lots is critical for ruggedness [5] [6].
Chromatographic Columns (C18, etc.)	Stationary phase for analyte separation.	Use columns from multiple lots or manufacturers in ruggedness testing. Column-to-column reproducibility is a critical ruggedness factor [3].
Buffer Salts & pH Modifiers (e.g., Formic Acid, Ammonium Acetate)	Control mobile phase pH and ionic strength to influence selectivity and peak shape.	Precise preparation and pH verification are vital. Small variations are tested in robustness studies [5] [4].
Chemical Reference Standards	To identify analytes and construct calibration curves for accuracy and linearity.	High purity (≥98%) and well-characterized. Stability of standard solutions should be verified [5].
Well-Characterized Sample	A homogeneous, stable test article used throughout validation.	Represents the actual sample matrix. Used to evaluate accuracy, precision, and to run all robustness/ruggedness experiments [5] [4].

Case Study: Validation of an HPLC Method for Quantifying Quercitrin in Peppers

A 2024 study developing an HPLC method to quantify quercitrin in Capsicum annuum L. provides an excellent example of validation principles in food analysis [5].

• Method Overview: The researchers established a reversed-phase HPLC method with diode array detection (DAD) for quantifying the flavonoid glycoside quercitrin in pepper extracts.
• Robustness Assessment: While the full robustness details are not included in the excerpt, the method's validation followed AOAC guidelines. Typically, such a study would involve varying parameters like mobile phase composition, flow rate, and column temperature to ensure the reported retention time and peak shape for quercitrin were consistent [5].
• Ruggedness (Intermediate Precision): The precision of the method was assessed through repeatability (multiple injections on the same day) and reproducibility (analysis on different days, potentially by different analysts). The method demonstrated excellent precision, with all Relative Standard Deviation (RSD) values falling within the acceptable AOAC criteria of ≤8% [5]. This confirms the method's ruggedness with respect to day-to-day and possible analyst-to-analyst variation.
• Key Results: The method showed strong linearity (R² > 0.9997) within the range of 2.5–15.0 μg/mL and accuracy with recovery rates between 89.02% and 99.30% [5]. This case underscores how integrating robustness and ruggedness testing yields a reliable, standardized method for quantifying bioactive compounds in food.

A precise understanding and rigorous application of robustness and ruggedness testing form the bedrock of a reliable analytical method. For HPLC applications in food science, where matrix complexity and regulatory demands are high, this distinction is not academic—it is practical and essential.

By systematically implementing the protocols outlined—using experimental designs for robustness and multi-factorial studies for ruggedness—scientists can transform a method from a prototype that works under ideal conditions into a robust tool that delivers consistent, trustworthy data anywhere, anytime. This commitment to methodological rigor ensures not only compliance with regulatory standards but also the advancement of credible and reproducible scientific research in food and pharmaceutical development.

Robustness testing is a critical component of the analytical procedure lifecycle, providing a measure of a method's capacity to remain unaffected by small, deliberate variations in method parameters. This capacity offers an indication of the method's reliability during normal usage and is embedded within the framework of major international regulatory guidelines from the International Council for Harmonisation (ICH), the United States Pharmacopeia (USP), and the U.S. Food and Drug Administration (FDA) [7] [3]. Although the specific terminology has evolved, the underlying imperative is consistent: to ensure that analytical methods are sufficiently rugged to withstand the minor variations expected in different laboratories, with different instruments, and over time, without compromising the validity of the results.

The ICH provides the most widely recognized definition, stating that "The robustness/ruggedness of an analytical procedure is a measure of its capacity to remain unaffected by small but deliberate variations in method parameters" [7] [4]. This evaluation is intended to identify factors that might cause variability and to establish appropriate system suitability test (SST) limits to ensure the procedure's validity is maintained whenever used [4]. Historically, the USP defined ruggedness as "the degree of reproducibility of test results obtained by the analysis of the same samples under a variety of normal test conditions," a concept closely aligned with what ICH and modern USP now categorize under intermediate precision and reproducibility [3]. A key distinction has emerged: if a parameter is written into the method, its variation is a robustness issue; if it is not specified (such as which analyst runs the method or on which specific instrument), it falls under ruggedness or intermediate precision [3].

Regulatory Definitions and Distinctions

Understanding the nuanced definitions and evolving terminology across different regulatory bodies is essential for proper method validation and regulatory compliance.

ICH Guidelines: Q2(R2) and Q14

The ICH guidelines provide the foundational framework for robustness testing in the pharmaceutical sector. The recent adoption of ICH Q2(R2) "Validation of Analytical Procedures" and ICH Q14 "Analytical Procedure Development" in 2025 further harmonizes and clarifies concepts related to robustness [8]. ICH training materials published in July 2025 emphasize a lifecycle approach to analytical procedures, outlining both minimal and enhanced approaches to development and validation [8]. A critical focus is placed on defining robustness and parameter ranges as part of the overall analytical procedure control strategy [8]. The ICH explicitly recommends that one consequence of robustness evaluation should be the establishment of a series of system suitability parameters to ensure the validity of the analytical procedure is maintained whenever used [4].

USP Chapter 1225: Validation of Compendial Methods

The USP has historically provided its own definitions, though recent revisions show a trend toward harmonization with ICH principles. The current USP guideline defines ruggedness as "the degree of reproducibility of test results obtained by the analysis of the same samples under a variety of normal test conditions," such as different laboratories, analysts, instruments, and reagent lots [3]. However, this term is falling out of favor with the USP, as evident in recently proposed revisions to Chapter 1225, where references to ruggedness have been deleted to harmonize more closely with ICH, using the term "intermediate precision" instead [3]. Both ICH and USP define the robustness of an analytical procedure as a measure of its capacity to remain unaffected by small, deliberate variations in procedural parameters listed in the method documentation [3].

FDA and Global Harmonization

The FDA acknowledges and participates in the ICH process, and the draft guidance on Q1 Stability Testing demonstrates the ongoing effort to consolidate and harmonize international requirements for pharmaceuticals [9]. The FDA's alignment with ICH guidelines means that the principles outlined in ICH Q2(R2) and Q14 are central to regulatory expectations for method validation [8]. The release of ICH training materials in 2025 is specifically aimed at supporting a "harmonised global understanding and consistent application of the new guidelines across ICH and non-ICH regions" [8].

Table 1: Evolution of Robustness and Related Terminology in Key Guidelines

Regulatory Body	Key Document	Traditional Term	Modern/Preferred Term	Core Focus
ICH	Q2(R2), Q14 (2025)	Robustness/Ruggedness	Robustness	Effect of small, deliberate variations in method parameters [7].
USP	Chapter <1225>	Ruggedness	Intermediate Precision	Reproducibility under a variety of normal conditions (labs, analysts, instruments) [3].
FDA	Supports ICH guidelines	-	Robustness & Lifecycle Approach	Aligns with ICH principles for a harmonized global framework [9] [8].

Experimental Design for Robustness Testing

A systematic, multivariate approach is recommended for conducting robustness tests. This involves identifying critical method parameters, deliberately varying them within a realistic range, and quantitatively evaluating their impact on method performance.

Selection of Factors and Levels

The first step is to select factors from the analytical method that are most likely to affect the results. For an HPLC method, these typically include:

• Mobile phase composition: pH, buffer concentration, organic modifier ratio [3] [10] [4].
• Chromatographic column: different batches or manufacturers [7] [4].
• Operating conditions: flow rate, column temperature, detection wavelength [7] [3] [4].

The extreme levels for each factor are chosen to be symmetrically (or occasionally asymmetrically) around the nominal level described in the method. The interval should be representative of the variations expected during method transfer. For example, the volume fraction of organic solvent (%B) in the mobile phase might be varied by ±1% to account for potential measurement errors during preparation [10]. For a quantitative factor, the uncertainty with which a level can be set is considered, and the interval is often defined as "nominal level ± k * uncertainty" where k is typically between 2 and 10 [7].

Figure 1: A generalized workflow for designing and executing a robustness test, from factor selection to conclusion drawing [7] [4].

Experimental Designs and Data Analysis

Screening designs are an efficient way to identify critical factors affecting robustness. The most common designs are Plackett-Burman and fractional factorial designs, which allow the study of a relatively large number of factors in a minimal number of experiments [3] [4]. For instance, a Plackett-Burman design with 12 runs can efficiently evaluate the effects of up to 11 factors [3].

The effect of each factor (E_X) on a response (Y) is calculated as the difference between the average responses when the factor is at its high level and the average when it is at its low level [7] [4]. The significance of these effects can be evaluated graphically using normal or half-normal probability plots, or statistically by comparing them to a critical effect derived from dummy factors or from the standard error of the effect [7] [4].

Table 2: Common Experimental Designs for Robustness Screening

Design Type	Number of Runs (N) for f factors	Key Characteristics	Best Use Case
Full Factorial	2^f	Examines all factor combinations; no confounding of effects [3].	Small number of factors (e.g., <5) where interactions are of interest [3].
Fractional Factorial	2^(f-p)	A fraction of the full factorial; effects are aliased (confounded) [3].	Larger number of factors; assumes some interactions are negligible [3].
Plackett-Burman	Multiple of 4 (e.g., 8, 12)	Very efficient for screening main effects; uses dummy factors for interpretation [7] [3].	Ideal for ruggedness testing where only main effects are of interest [3].

Application in HPLC for Food and Pharmaceutical Analysis

The principles of robustness are universally applicable across analytical chemistry, but they take on specific importance in regulated environments like pharmaceutical and food analysis.

The Scientist's Toolkit: Essential Materials for an HPLC Robustness Study

A well-executed robustness study requires careful selection of materials and reagents to ensure the findings are meaningful and applicable to routine use.

Table 3: Key Research Reagent Solutions and Materials for HPLC Robustness Testing

Item	Function/Description	Considerations for Robustness Testing
HPLC System	Instrument for performing separation and detection.	Different systems or models can be a ruggedness factor [3].
Chromatographic Column	Stationary phase for chemical separation.	A key factor; test different batches or from different manufacturers [7] [4].
Organic Solvents (HPLC Grade)	Mobile phase components (e.g., Acetonitrile, Methanol).	Purity and supplier can be varied as a qualitative factor [4].
Buffer Salts & Reagents	For preparing mobile phase with specific pH and ionic strength.	Different lots or suppliers can be a factor [4].
Reference Standards	Highly characterized substances for quantification.	Use a single, well-characterized lot for the entire study to avoid variability.
Test Sample	The material to be analyzed (e.g., drug product, food sample).	Should be homogeneous and stable for the duration of the study.

Case Study: Robustness in an HPLC Assay

A practical example involves an HPLC assay for an active compound and related substances in a drug formulation [7]. Eight factors were selected, including mobile phase pH, column temperature, flow rate, and detection wavelength, each examined at two levels in a Plackett-Burman design with 12 experiments. Responses measured included the percent recovery of the active compound (an assay response) and the critical resolution between the active compound and a related substance (a system suitability response). The effects of each factor were calculated, and statistically non-significant effects on the percent recovery confirmed the method's robustness for quantitative analysis. Significant effects on the critical resolution were used to define scientifically justified system suitability test limits [7] [4].

In food analysis, the principles of Green Analytical Chemistry (GAC) are increasingly integrated with robustness. Methods are being developed that not only are robust but also minimize environmental impact by reducing hazardous solvent use, exemplified by the development of high-throughput green analytical testing technologies (HT-GATTs) [11]. A recent RP-HPLC method for simultaneously quantifying curcumin and dexamethasone in polymeric micelles highlights these trends, emphasizing a short runtime, isocratic elution with common solvents, and compliance with ICH validation standards, including robustness [12].

Robustness testing is not merely a regulatory checkbox but a fundamental activity that ensures the reliability and longevity of an analytical method. The harmonized guidelines from ICH, USP, and FDA underscore its critical role in the method lifecycle. By identifying critical method parameters early—through well-designed experiments such as Plackett-Burman or fractional factorial designs—scientists can define a robust operational space and establish meaningful system suitability criteria. This proactive approach, whether applied to sophisticated pharmaceutical products or complex food matrices, prevents future failures during method transfer and routine use, ultimately saving time and resources while ensuring the generation of reliable, high-quality data that meets global regulatory standards.

In the realm of food analysis, robustness of an analytical method is defined as a measure of its capacity to remain unaffected by small, deliberate variations in procedural parameters listed in the documentation, providing an indication of its reliability during normal use [3]. For High-Performance Liquid Chromatography (HPLC) methods, which are extensively used for quantifying nutrients, detecting contaminants, and authenticating food products, establishing robustness is not merely a technical formality but a fundamental prerequisite for data integrity and successful method transfer between laboratories [13] [14].

The failure to establish method robustness can lead to severe consequences, including delayed product releases, costly retesting, regulatory non-compliance, and ultimately, a loss of confidence in analytical data [14] [15]. In food safety, where the detection of a contaminant at trace levels can trigger massive product recalls, a method that is not robust may yield inconsistent results under normal operational variations, risking public health and incurring significant financial losses. This application note delineates the critical role of robustness testing, provides a detailed protocol for its implementation in food HPLC methods, and synthesizes key performance data to guide researchers and scientists in strengthening their analytical workflows.

The Critical Role of Robustness: Data Integrity and Method Transfer

Upholding Data Integrity

Data integrity is the cornerstone of reliable analytical results. Regulatory agencies have highlighted that a substantial percentage of warning letters are linked to data integrity issues [15]. A robust method is inherently less susceptible to the minor, unforeseen variations that occur in any laboratory environment, such as subtle fluctuations in mobile phase pH, temperature, or solvent composition. When a method is not robust, analysts might be compelled to repeatedly adjust integration parameters or modify the procedure to obtain acceptable results. Such practices, often described as "playing with integration parameters," can be interpreted as data manipulation, thereby compromising data integrity [15]. A robust method ensures that results are reproducible and accurate the first time, supporting confident decision-making in food quality control and safety assurance.

Ensuring Successful Method Transfer

The process of analytical method transfer is a documented qualification proving that a receiving laboratory can execute an analytical procedure with equivalent accuracy, precision, and reliability as the transferring laboratory [14]. A poorly executed transfer leads to significant operational delays and costs. Robustness is a key predictor of transfer success. If the effects of method parameters are well-understood and controlled, the method will perform consistently even when different analysts, equipment, or reagent lots are used—factors that fall under the related concept of ruggedness or intermediate precision [3].

Investigating robustness during method development identifies critical parameters and establishes system suitability criteria, which are vital for qualifying the entire analytical system before routine use [3]. This proactive investment saves considerable time and resources during later method transfer and validation phases. As noted in industry polls, a significant number of laboratories face system suitability test failures weekly, and a primary strategy for improving transfer success is the modernization and robust optimization of methods [15].

Experimental Protocol for Robustness Testing of an HPLC Method

This protocol is designed to systematically evaluate the robustness of an HPLC method intended for the analysis of a food contaminant or bioactive compound, following a Quality by Design (QbD) framework.

Pre-Experimental Planning

• Define Critical Method Parameters: Identify factors likely to impact method performance based on developmental knowledge. For a reversed-phase HPLC method, typical factors include:
  • Mobile phase pH: A critical factor for ionizable compounds.
  • Mobile phase composition (%B): The volume fraction of the organic solvent.
  • Column temperature: Influences retention time and separation efficiency.
  • Flow rate: Affects backpressure and retention.
  • Wavelength of detection [3] [10].
• Define Critical Analytical Attributes (Output Responses): These are the measurable indicators of method performance. Key attributes include:
  • Resolution (Rs) of a critical peak pair.
  • Retention time (tR) of the target analyte.
  • Tailing factor (Tf).
  • Theoretical plate count (N) [16] [17].
• Select an Experimental Design: A screening design is the most efficient approach for robustness studies. A Plackett-Burman design or a fractional factorial design is recommended to investigate multiple factors with a minimal number of experimental runs [3]. For example, a Plackett-Burman design in 12 runs can screen up to 11 factors.
• Set Factor Levels: Choose realistic "high" and "low" levels for each factor based on expected variations in a routine lab setting. For instance, the volume fraction of organic solvent (%B) might be varied by ±1% from the nominal value to account for potential preparation errors [10]. Similarly, buffer pH can be varied by ±0.1 units, temperature by ±2°C, and flow rate by ±0.1 mL/min.
• Develop a Validation Protocol: A detailed protocol is essential. It should specify the objectives, experimental design, acceptance criteria for all analytical attributes, procedures for sample and mobile phase preparation, and the statistical evaluation plan [14].

Experimental Workflow

The following diagram illustrates the sequential workflow for conducting a robustness study.

Execution and Data Analysis

• Preparation: Prepare the mobile phase and standard solutions at the nominal composition and at the extremes defined in the experimental design [17].
• Execution: Perform the chromatographic runs as specified by the experimental design matrix. It is crucial to randomize the run order to avoid systematic bias.
• Data Collection: For each run, record the predefined critical analytical attributes (e.g., retention time, resolution, tailing factor) for the target analyte(s).
• Data Analysis:
  • Statistical Evaluation: Use statistical tools like Analysis of Variance (ANOVA) to determine which factors have a statistically significant effect on the responses [17] [18].
  • Comparison with Acceptance Criteria: Evaluate the data against the pre-defined acceptance criteria. A method is considered robust if all analytical attributes remain within specified limits (e.g., resolution > 2.0, tailing factor < 2.0) across all experimental variations [14].
• Reporting: Document the entire study in a robustness report. The report should conclude whether the method is robust over the studied parameter ranges and define the system suitability parameters that will ensure ongoing method performance [3].

Data Presentation and Analysis

The following table consolidates key robustness findings from recent studies, illustrating typical factors, their variations, and the impact on critical method attributes.

Table 1: Consolidated Robustness Data from HPLC Method Studies

Method Application	Factor Investigated	Variation Level	Impact on Key Attribute	Outcome & Acceptability
Favipiravir Analysis [16]	Column Type (C18), Buffer pH, Solvent Ratio	Deliberate variations via DoE	Retention Time (Y2), Tailing Factor (Y3), Theoretical Plates (Y4)	Method was robust (RSD < 2%); A Method Operable Design Region (MODR) was established.
Domiphen Bromide in Pharmaceuticals [17]	Acetonitrile Ratio, Flow Rate, Column Temperature	Variations via Full Factorial DoE	Retention, Resolution, Peak Shape	ANOVA confirmed factor influence; method was optimized and deemed robust.
General HPLC Guideline [10]	Organic Solvent (%B) in Mobile Phase	± 1% from nominal	Retention Factor (k), Resolution (Rs)	A 1% change in %B can alter retention by a factor of ~3; critical for low-resolution separations.

Risk Assessment and System Suitability

A key outcome of robustness testing is the identification of high-risk factors that must be controlled through system suitability tests (SSTs). The diagram below maps the relationship between risk factors, their effects on the analysis, and the corresponding controls.

The Scientist's Toolkit: Essential Reagents and Materials

Table 2: Essential Research Reagent Solutions for Robustness Testing

Item	Function in Robustness Testing	Considerations for Food Applications
HPLC-Grade Solvents (Acetonitrile, Methanol)	Mobile phase components; variations in grade or supplier can affect UV background and retention.	Low UV cutoff for trace analysis; ensure compatibility with MS detection if used.
High-Purity Water (e.g., 18 MΩ·cm)	Aqueous component of mobile phase; reduces background noise and column contamination.	Must be free of organic contaminants and ions that could interfere with analysis.
Buffer Salts & pH Adjusters (e.g., Phosphate salts, Perchloric acid, Ammonium formate)	Control mobile phase pH, critical for reproducibility of ionizable compounds.	Buffer concentration and pH should be varied as a robustness factor [17]. Use volatile salts for LC-MS.
Characterized Reference Standard	The benchmark for identifying the target analyte and assessing method performance attributes.	Purity must be certified; should be representative of the analyte in the food matrix.
Inertsil ODS-3 C18 Column	Stationary phase for reversed-phase separation; a key factor in robustness [16] [17].	Test different column batches/lots as part of robustness. Document column dimensions and particle size.
Sample Preparation Solvents (e.g., Ethanol)	Used to extract the analyte from the food matrix [17].	Select for efficiency and greenness; ensure compatibility with the mobile phase to avoid precipitation.

Robustness testing is an indispensable element of the analytical method lifecycle, serving as the bridge between method development and its reliable application in quality control and multi-laboratory use. For food scientists, investing in a systematic robustness study, guided by QbD principles and appropriate experimental designs, is a non-negotiable practice. It directly safeguards data integrity by ensuring methods produce consistent results under normal operational variations and dramatically increases the success rate of method transfers. By implementing the protocols outlined in this document, researchers and drug development professionals can build a foundation of confidence in their HPLC methods, ensuring they are fit-for-purpose in guaranteeing food safety, quality, and authenticity.

The development of a robust High-Performance Liquid Chromatography (HPLC) method for food matrices requires a systematic approach to identify and control critical method variables. Robustness, defined as the capacity of an analytical procedure to remain unaffected by small, deliberate variations in method parameters, is a vital indicator of the method's reliability during normal usage and its successful transfer between laboratories [3]. This application note details the core parameters requiring evaluation for HPLC methods in complex food matrices, provides a standardized protocol for robustness testing using experimental design, and presents a comprehensive toolkit for implementation. Integrating these practices into method development ensures consistent performance, facilitates regulatory compliance, and enhances the quality control of food products.

In food analysis, HPLC methods are routinely employed to quantify everything from nutrients and additives to contaminants and authenticity markers. The complex and variable nature of food matrices—ranging from powdered drinks to high-fat content products—poses significant analytical challenges. A method that performs adequately under ideal, controlled conditions may fail when subjected to the slight, inevitable variations encountered in daily laboratory practice, such as different reagent lots, instrument configurations, or ambient temperature fluctuations [3] [19].

Robustness testing is therefore not merely a final validation check but an integral part of the method development lifecycle. Investigating robustness "during development makes sense as parameters that affect the method can be identified easily when manipulated for selectivity or optimization purposes" [3]. A proactive investment in robustness testing saves considerable time, energy, and expense later by preventing method failure during validation or routine use. This document frames robustness testing within a broader research thesis, positing that a systematic, Quality by Design (QbD)-driven identification of critical method variables is fundamental to developing reliable HPLC methods for food applications.

Identifying Critical Method Variables in Food Matrices

The first step in robustness testing is to identify which parameters are most likely to impact method performance. These parameters are typically the ones specified within the method documentation. The following table summarizes the core parameters, their typical variations, and their matrix-specific considerations in food analysis.

Table 1: Critical HPLC Method Variables and Their Impact on Food Analysis

Parameter Category	Specific Variables	Typical Variation Range	Considerations for Food Matrices
Mobile Phase	pH of aqueous buffer	± 0.1 - 0.2 units [19]	Critical for ionizable analytes (e.g., organic acids, preservatives like benzoic acid). Affects retention time and selectivity.
	Buffer Concentration	± 2 - 5 mM [20]	Can impact peak shape and retention, especially for ionizable compounds.
	Organic Modifier Ratio	± 2 - 3% (v/v) [3]	Significantly affects retention (k) and resolution (Rs). A key variable in gradient elution [19].
Chromatographic Column	Column Temperature	± 2 - 5 °C [20]	Affects retention, efficiency, and backpressure. Particularly important for viscous matrices.
	Different Column Lots/Brands	Same stationary phase from different lots/vendors	Subtle differences in ligand density, endcapping, or silica purity can alter selectivity and resolution.
Flow Dynamics	Flow Rate	± 0.05 - 0.1 mL/min [20]	Directly impacts retention time, backpressure, and can influence efficiency.
Detection	Wavelength (UV/Vis)	± 2 - 3 nm [3]	Affects sensitivity and linearity for multi-component analysis (e.g., simultaneous determination of additives with different λ_max) [19].

The relationship between these parameters and the overall method robustness can be conceptualized as a multi-factorial system. The following diagram illustrates the logical workflow for identifying, testing, and controlling these critical variables.

Experimental Protocol: Robustness Evaluation Using a Screening Design

This protocol provides a step-by-step methodology for evaluating the robustness of an HPLC method for determining food additives (e.g., sweeteners, preservatives, colorants) in a powdered drink matrix, based on established chemometric practices [3] [19] [21].

Principle

A Plackett-Burman screening design will be used to efficiently identify which of several method parameters have a significant effect on predefined Critical Method Attributes (CMAs), such as resolution, retention time, and peak area. This design is highly economical for evaluating a large number of factors with a minimal number of experimental runs [3] [21].

Research Reagent and Instrumentation Toolkit

Table 2: Essential Materials and Equipment for Robustness Testing

Item Category	Specific Examples	Function/Role in Protocol
HPLC System	Shimadzu or Agilent HPLC with DAD	Provides precise solvent delivery, sample introduction, separation, and multi-wavelength detection.
Chromatography Column	C18 column (e.g., 150 mm x 4.6 mm, 5 µm)	The stationary phase where chromatographic separation occurs.
Chemical Standards	Acesulfame K, Benzoic Acid, Tartrazine, Caffeine [19]	High-purity reference materials for identifying and quantifying target analytes.
Mobile Phase Components	Ammonium Acetate, Methanol (HPLC grade), Acetonitrile (HPLC grade), Glacial Acetic Acid	Constitute the eluent that carries the sample through the column. Composition and pH are critical variables.
Sample Preparation	Analytical Balance, pH Meter, Volumetric Flasks, 0.45 µm Nylon Syringe Filters	Ensures accurate and reproducible preparation of standards and samples, free of particulates.
Software	Design Expert or Fusion QbD [22]	Facilitates the design of experiments, data analysis, and multi-response optimization.

Procedure

Step 1: Define Factors and Levels Select 5-7 critical parameters from Table 1. For each parameter, define a nominal (center) value, as well as high (+) and low (-) levels that represent small, realistic variations expected in routine use. Example for a food additive method:

• Factor A: pH of mobile phase (Nominal: 3.5, Low: 3.4, High: 3.6)
• Factor B: % Methanol at gradient start (Nominal: 8.5%, Low: 8.0%, High: 9.0%)
• Factor C: Column Temperature (Nominal: 30°C, Low: 28°C, High: 32°C)
• Factor D: Flow Rate (Nominal: 1.0 mL/min, Low: 0.95 mL/min, High: 1.05 mL/min)

Step 2: Select an Experimental Design A Plackett-Burman design for 4 factors can be executed in 12 randomized runs. This design is ideal for estimating the main effects of each factor without confounding them with two-factor interactions for this number of factors [3].

Step 3: Execute the Experiments Prepare the mobile phases and set the instrument parameters according to the randomized run order specified by the design. Inject a standard solution containing all target analytes (e.g., a mixture of seven food additives and caffeine [19]) in each experimental condition.

Step 4: Record Responses For each chromatographic run, record the following CMAs for all critical peak pairs:

• R_s: Resolution between the most critical peak pair.
• t_R: Retention time of the last eluting peak (for analysis time).
• T: Peak tailing factor for the main analyte.
• N: Plate count for the main analyte.

Step 5: Analyze Data and Identify Critical Variables Statistically analyze the data using ANOVA or by calculating the effect of each factor. A Pareto chart of effects is useful for visualizing which parameters have a statistically significant impact (p < 0.05) on the responses. Factors that significantly affect critical responses like resolution are deemed critical and must be tightly controlled in the final method.

Case Study and Data Presentation

A study optimizing an HPLC-DAD method for seven food additives and caffeine in powdered drinks utilized a Box-Behnken Design to optimize three factors: initial methanol percentage, final methanol percentage, and mobile phase pH [19]. The robustness of the optimized method was then verified by introducing small, deliberate variations in the flow rate and column temperature. The results of such a robustness test can be summarized as follows:

Table 3: Example Robustness Test Results for a Food Additive HPLC Method Hypothetical data based on [20] and [19]

Variation Parameter	Condition	Retention Time (min) RSD%	Resolution (Rs) RSD%	Peak Area RSD%
Flow Rate (Nominal: 1.0 mL/min)	0.9 mL/min	0.85	1.12	0.45
	1.1 mL/min	0.91	1.08	0.51
Column Temperature (Nominal: 30°C)	28°C	0.78	1.25	0.38
	32°C	0.82	1.31	0.42
Mobile Phase pH (Nominal: 3.5)	3.4	1.05	1.95	0.61
	3.6	1.12	2.10	0.58
Acceptance Criterion	-	< 2.0%	< 2.0%	< 2.0%

In this example, all variations resulted in Relative Standard Deviations (RSD%) below the typical acceptance criteria of 2.0%, demonstrating that the method is robust for the tested parameters. The slightly higher RSD% for resolution under pH variations indicates that this parameter is more critical and should be carefully controlled during mobile phase preparation.

Identifying and controlling critical method variables is paramount for developing robust HPLC methods suitable for the complex and varied world of food analysis. By adopting a systematic approach grounded in experimental design, researchers and scientists can move beyond univariate, trial-and-error methods. The protocols and tools outlined in this application note—from the initial identification of parameters to the final statistical analysis—provide a robust framework for ensuring method reliability. This rigorous practice not only guarantees the quality and safety of food products but also streamlines method transfer and strengthens regulatory submissions, forming a cornerstone of modern analytical quality assurance in food science and drug development.

Robustness testing is a critical, systematic component of analytical method validation that measures a procedure's capacity to remain unaffected by small, deliberate variations in method parameters [7]. In high-performance liquid chromatography (HPLC) for food applications, this evaluation provides an indication of a method's reliability during normal usage and transfer between laboratories, instruments, and analysts. The International Conference on Harmonization (ICH) formally defines robustness/ruggedness as "a measure of its capacity to remain unaffected by small but deliberate variations in method parameters and provides an indication of its reliability during normal usage" [7]. For quality control laboratories analyzing food components, contaminants, or bioactive compounds, undetected method vulnerabilities can lead to costly failures including product recalls, regulatory non-compliance, and erroneous safety conclusions.

The strategic implementation of robustness testing during method optimization—rather than after validation—enables proactive identification of factors causing variability in assay responses [7]. This approach is particularly valuable for HPLC methods in food analysis, where complex matrices and stringent regulatory requirements demand methods that maintain performance despite minor, inevitable fluctuations in laboratory conditions. The principles of Analytical Quality by Design (AQbD) formalize this approach, incorporating risk assessment to identify factors with significant impact on method performance and establishing a method operable design region (MODR) where method performance remains consistent [16].

Theoretical Framework and Regulatory Foundation

The Science Behind Robustness Testing

Robustness testing operates on the principle that analytical methods exist within ecosystems of controllable and uncontrollable variables. For HPLC methods in food analysis, these variables span environmental conditions, reagent suppliers, instrument configurations, and operator techniques. The primary objective of robustness testing is to distinguish between acceptable parameter fluctuations that negligibly impact results and critical variations that compromise method integrity.

The experimental basis for robustness testing relies on estimating factor effects through carefully designed experiments. The effect of a factor (X) on a response (Y) is calculated as the difference between the average responses when the factor is at its high level and low level respectively [7]. This quantitative approach transforms subjective assessments into data-driven decisions about method suitability, providing a scientific foundation for establishing system suitability test (SST) limits [7].

Regulatory Expectations and Standards

While robustness testing is mandatory for pharmaceutical methods under ICH guidelines, the food industry increasingly adopts these standards through voluntary compliance with ISO 14001, ISO 22000, and global food safety initiatives [13]. Regulatory bodies expect methods to perform consistently when transferred between laboratories, a requirement that necessitates thorough understanding of method robustness prior to inter-laboratory studies.

The United States Pharmacopeia (USP) definition, while focused on pharmaceuticals, provides guidance applicable to food analysis: "The ruggedness of an analytical method is the degree of reproducibility of test results obtained by the analysis of the same sample under a variety of normal test conditions, such as different laboratories, different analysts, different instruments, different lots of reagents, different elapsed assay times, different assay temperatures, different days etc." [7]. This comprehensive view of reproducibility underscores the importance of robustness testing in preventing methodological failures in quality control environments.

Experimental Design for HPLC Robustness Testing

Factor Selection and Level Determination

The first step in designing a robustness test involves selecting factors and their appropriate levels. Factors should be chosen based on their potential impact on method performance and their likelihood of variation during routine use.

Table 1: Factors and Levels for HPLC Robustness Testing

Factor Type	Factor	Low Level (-1)	Nominal Level (0)	High Level (+1)
Quantitative	Mobile phase pH	pH 3.0	pH 3.1	pH 3.2
Quantitative	Column temperature	28°C	30°C	32°C
Quantitative	Flow rate	0.9 mL/min	1.0 mL/min	1.1 mL/min
Quantitative	Detection wavelength	321 nm	323 nm	325 nm
Mixture-related	Organic modifier fraction	17%	18%	19%
Qualitative	Column manufacturer	Supplier A	Nominal Supplier	Supplier B
Qualitative	Buffer lot	Lot A	Nominal Lot	Lot B

For quantitative factors, extreme levels are typically chosen symmetrically around the nominal level, with intervals representative of expected variations during method transfer [7]. The interval can be defined as "nominal level ± k * uncertainty" where k ranges from 2 to 10, with the uncertainty based on the largest absolute error for setting a factor level [7]. Asymmetric intervals may be appropriate when response changes are not linear, such as with detection wavelength where absorbance may peak at a specific value [7].

Experimental Design Selection

Two-level screening designs, particularly fractional factorial (FF) or Plackett-Burman (PB) designs, are most appropriate for robustness testing [7]. These designs efficiently examine multiple factors in a minimal number of experiments.

• Fractional Factorial Designs: Number of experiments (N) is a power of two (8, 16, 32...)
• Plackett-Burman Designs: N is a multiple of four, allowing examination of up to N-1 factors

For example, a robustness test examining 8 factors can be conducted using a 12-experiment PB design [7]. When the maximum number of factors is not examined, remaining columns can be assigned as dummy factors to assist in statistical interpretation.

Response Selection

Both assay responses and system suitability test (SST) responses should be monitored during robustness testing:

Table 2: Response Metrics for HPLC Robustness Testing

Response Category	Specific Responses	Acceptance Criteria
Assay Responses	Content/Concentration of analytes	No significant effects from varied parameters
SST Responses	Retention time	RSD 〈 2%
SST Responses	Theoretical plate count	〉 2000
SST Responses	Tailing factor	〈 2.0
SST Responses	Critical resolution	〉 1.5

For food analysis methods, particularly those quantifying multiple compounds (e.g., bioactive compounds, contaminants), all critical peak responses should be monitored, including resolution between adjacent peaks [16] [13].

Protocol: Robustness Testing for HPLC Methods in Food Analysis

Materials and Equipment

Table 3: Essential Research Reagent Solutions and Materials

Item	Specification	Function in Robustness Testing
HPLC System	With DAD/UV-Vis detector, column oven, and auto-sampler	Method execution under controlled parameters
Chromatographic Column	Inertsil ODS-3 C18 (250 mm × 4.6 mm, 5 μm) or equivalent	Stationary phase; alternative columns test selectivity robustness
Mobile Phase Components	Acetonitrile (HPLC grade), disodium hydrogen phosphate anhydrous (reagent grade)	Creates elution environment; variations test mobile phase robustness
Reference Standards	Certified reference materials of target analytes (e.g., favipiravir)	System qualification and response monitoring
Buffer Solutions	20 mM disodium hydrogen phosphate, pH adjusted with phosphoric acid	Tests robustness to mobile phase pH variations
Sample Material	Representative food matrix with known analyte concentration	Assesses method performance in realistic conditions

Experimental Procedure

Step 1: Risk Assessment and Factor Identification

• Conduct failure mode and effects analysis (FMEA) to identify factors with potential impact on method performance
• Prioritize factors based on severity, occurrence, and detection rankings
• Select 5-8 high-risk factors for experimental evaluation [16]

Step 2: Experimental Design Implementation

• Select appropriate experimental design (Plackett-Burman or fractional factorial) based on number of factors
• Generate experimental sequence, considering randomization or anti-drift sequences
• For time-sensitive factors (e.g., column aging), incorporate replicated nominal experiments at regular intervals to monitor and correct for drift effects [7]

Step 3: Experimental Execution

• Prepare mobile phases, standards, and samples according to nominal method specifications
• Execute experimental runs in predetermined sequence
• For each experiment, prepare fresh solutions using the parameter combinations specified in the design matrix
• Analyze one blank solution, one reference standard solution, and one sample solution representing the typical food matrix for each experimental run [7]

Step 4: Data Collection

• Record all system suitability parameters for each run
• Document assay responses (peak areas, retention times, resolutions) for all target analytes
• Capture chromatographic performance metrics (theoretical plates, tailing factors)

Step 5: Data Analysis and Effect Calculation

• Calculate factor effects for each response using the formula: ( Ex = \frac{\sum Y{(+)}}{N{(+)}} - \frac{\sum Y{(-)}}{N{(-)}} ) where ( Ex ) is the effect of factor X, ( Y{(+)} ) and ( Y{(-)} ) are responses at high and low factor levels, and ( N{(+)} ) and (N{(-)} ) are the number of experiments at respective levels [7]
• Use graphical methods (normal probability plots, half-normal probability plots) to identify significant effects
• Apply statistical analysis (Dong's algorithm or dummy factor method) to determine critical effects and significance levels [7]

Step 6: Method Operable Design Region (MODR) Definition

• Establish acceptable ranges for each factor where method performance remains within acceptance criteria
• Document MODR boundaries for method control strategy
• For factors with significant effects, define tighter control limits in standard operating procedures

Application Example: Favipiravir Quantification Method

A recent study demonstrated the AQbD approach for developing a robust RP-HPLC method for favipiravir quantification [16]. The risk assessment identified three high-level risk factors: (X1) ratio of solvent, (X2) pH of the buffer, and (X3) column type. Researchers studied their impact on output responses (Y1: peak area, Y2: retention time, Y3: tailing factor, Y4: theoretical plates) using a d-optimal experimental design. The MODR was calculated using Monte Carlo simulation, and the final method conditions were established as: Inertsil ODS-3 C18 column (250 mm, 4.6 mm, 5 μm); mobile phase of acetonitrile:disodium hydrogen phosphate anhydrous buffer (pH 3.1, 20 mM) in 18:82 v/v ratio; isocratic flow rate of 1 mL/min at 30°C with DAD detection at 323 nm [16]. The method validation confirmed excellent precision, accuracy, and robustness with RSD value 〈 2%, successfully applying the method to quantify favipiravir in laboratory-prepared tablets [16].

Robustness testing workflow for HPLC methods

Data Analysis and Interpretation

Statistical Treatment of Results

The statistical interpretation of robustness test data focuses on distinguishing meaningful factor effects from random variation. Two primary approaches are recommended:

• Graphical Analysis: Normal probability plots or half-normal probability plots display absolute effects against their cumulative normal probabilities. Effects that deviate from the straight line formed by most points are considered potentially significant [7].

• Statistical Significance Testing: Critical effects can be determined using:

  • Dummy Factor Method: When dummy factors are included in the design, the standard deviation of dummy effects estimates experimental error, establishing a threshold for significance [7].
  • Dong's Algorithm: An iterative procedure that eliminates extreme effects when estimating the standard error, then calculates a critical effect value ((E_{critical})) at a specified significance level (typically α = 0.05) [7].

For the HPLC example examining eight factors in a PB design with three dummy factors, the effect of each factor on percent recovery and critical resolution can be calculated as shown in Table 2 of the search results [7]. The dummy factors provide an estimate of experimental error, allowing comparison to determine whether factor effects exceed natural variation.

Establishing System Suitability Test Limits

Robustness test results directly inform appropriate system suitability test limits. For factors identified as significant, the experimental data shows how response metrics change across the tested range. This enables setting scientifically justified SST limits that ensure method performance while allowing for normal operational variations.

For example, if mobile phase pH variations from 3.0 to 3.2 cause retention time changes of ±0.3 minutes, the SST limit for retention time can be set at nominal ±0.5 minutes to provide a safety margin while accommodating normal method operation.

Green Analytical Chemistry Considerations

Integration with Sustainability Principles

Robustness testing aligns with the principles of Green Analytical Chemistry (GAC), particularly Principle 7 (energy efficiency) and Principle 12 (greenness assessment) [13]. By identifying optimal method conditions that tolerate minor variations, robustness testing reduces method failures and associated reagent waste. The greenness of HPLC methods can be assessed using tools such as:

• Analytical Eco-Scale: A penalty-point-based system evaluating hazardous chemicals, waste generation, energy consumption, and occupational hazards [16] [13]. Methods scoring 〉75 are considered excellent green methods, as demonstrated by the favipiravir quantification method which achieved this benchmark [16].

• AGREE Metric: Integrates all 12 GAC principles into a holistic algorithm providing a single-score evaluation with intuitive graphic output [13].

• Green Analytical Procedure Index (GAPI): Provides visual, semi-quantitative evaluation of the entire analytical workflow through a color-coded pictogram [13].

Green Method Optimization Strategies

Robustness testing supports green method optimization through:

• Solvent Reduction: Identifying the narrowest possible mobile phase composition ranges that maintain robustness, minimizing solvent consumption
• Energy Efficiency: Optimizing column temperature and flow rate to reduce energy consumption while maintaining separation efficiency
• Waste Minimization: Establishing robust method conditions that reduce failed runs and associated waste

The application of green metrics to HPLC methods for food analysis demonstrates how robustness testing contributes to both reliability and sustainability goals in quality control laboratories [13].

Robustness testing represents a proactive investment in method reliability that prevents costly errors in quality control laboratories. For HPLC methods in food applications, systematic evaluation of method factors identifies vulnerabilities before they impact product quality or safety decisions. The experimental design approach outlined in this protocol enables efficient assessment of multiple factors while providing statistical confidence in results. When integrated with AQbD principles and green chemistry considerations, robustness testing delivers methods that are not only reliable but also sustainable and transferable across laboratories and instrument platforms. As regulatory expectations and analytical challenges evolve, robustness testing remains essential for preventing the substantial costs of method failure—from product recalls to erroneous safety conclusions—ensuring that quality control laboratories deliver accurate, dependable results despite the normal variations inherent in analytical practice.

From Theory to Practice: Designing and Executing Robustness Studies for Complex Food Matrices

Within the context of robustness testing for High-Performance Liquid Chromatography (HPLC) methods in food applications, systematic parameter selection is paramount. Method robustness is formally defined as a measure of the analytical procedure's capacity to remain unaffected by small, deliberate variations in method parameters, providing an indication of its reliability during normal usage [3]. For food analysts and drug development professionals, establishing a robust method is a critical step in validation, ensuring that methods transfer successfully between laboratories and instruments while producing reliable data for food safety, quality control, and regulatory compliance.

This application note provides a detailed framework for the experimental selection and robustness testing of four critical HPLC parameters: flow rate, mobile phase pH, temperature, and column variations. The protocols are designed to be integrated into a broader thesis on HPLC method validation, with a specific focus on challenges encountered in complex food matrices.

Theoretical Foundations of Parameter Selection

The Role of System Parameters in HPLC Separation

The selectivity and efficiency of an HPLC separation are governed by the complex interplay between the mobile phase, stationary phase, and analytes. Key parameters directly influence the fundamental chromatographic outcomes:

• Flow Rate: Primarily affects the analysis time and column backpressure. According to the van Deemter equation, an optimal flow rate minimizes band broadening to maximize efficiency. Excessively high flows increase pressure and can reduce efficiency, while low flows prolong analysis time [23] [24].
• Mobile Phase pH: Critically controls the ionization state of ionizable analytes. A shift in pH can dramatically alter the retention factor (k) of acids, bases, and amphoteric compounds, which is common in food analysis (e.g., organic acids, alkaloids, amino acids). The buffer capacity of the mobile phase must be sufficient to maintain the selected pH [3].
• Temperature: Influences several physical properties. Higher temperatures reduce mobile phase viscosity (lowering backpressure) and accelerate mass transfer (improving efficiency). Temperature also affects the retention factor and can alter selectivity, as described by the van't Hoff relationship [23].
• Column Variations: Represents a major source of method ruggedness issues. Different column batches, ages, or brands with nominally identical stationary phases can vary in silica surface chemistry, ligand density, and endcapping, leading to shifts in retention time and selectivity [3] [25].

Robustness vs. Ruggedness

It is crucial to distinguish between two related but distinct concepts:

• Robustness: Measures the method's resilience to small, deliberate changes in internal parameters specified in the method (e.g., pH ± 0.1 units, temperature ± 2°C). This is the focus of the experimental protocols herein [3].
• Ruggedness: Refers to the degree of reproducibility of test results under external variations, such as different laboratories, analysts, or instruments. The USP now harmonizes this under "intermediate precision" [3].

Experimental Design for Robustness Testing

A well-designed robustness study proactively identifies critical parameters and establishes system suitability limits, preventing future method failure during transfer or routine use.

Screening Designs

Screening designs are efficient for evaluating the larger number of factors typical in chromatographic methods. They identify which factors have significant effects on critical method responses (e.g., retention time, resolution, peak area) [3].

The most common screening designs are:

• Full Factorial Designs: Test all possible combinations of factors at their high and low levels. For k factors, this requires 2k runs. This design is comprehensive but becomes impractical with more than 4-5 factors [3].
• Fractional Factorial Designs: A carefully selected subset (e.g., 1/2, 1/4) of the full factorial combinations. This is a highly efficient approach for screening 5 or more factors, as it significantly reduces the number of experimental runs, though some interaction effects may be confounded [3].
• Plackett-Burman Designs: Very economical designs for evaluating main effects when interactions are negligible. They are run in multiples of four and are ideal for identifying the most critical factors from a large set with minimal experimental effort [3].

Table 1: Comparison of Experimental Screening Designs for Robustness Testing

Design Type	Number of Runs for 4 Factors	Number of Runs for 7 Factors	Key Advantages	Key Limitations
Full Factorial	16	128	No confounding of effects; estimates all interactions.	Number of runs becomes prohibitive with many factors.
Fractional Factorial	8 (½ fraction)	32 (¼ fraction)	Highly efficient; practical for many factors.	Some interactions are confounded (aliased) with main effects.
Plackett-Burman	12	12	Extremely efficient for screening a large number of factors.	Only main effects are estimated; all interactions are confounded.

Defining Parameter Ranges

The variations tested should reflect the expected variations in a routine laboratory environment. The limits should be small but realistic, for instance:

• Flow Rate: Nominal value ± 0.1 mL/min
• Mobile Phase pH: Nominal value ± 0.1 units
• Column Temperature: Nominal value ± 2–5°C
• Column Variations: Different batches from the same supplier, or equivalent columns from different suppliers [3].

Detailed Experimental Protocols

Protocol 1: Robustness Screening for Critical Parameters

This protocol uses a Plackett-Burman design to efficiently screen the impact of flow rate, pH, temperature, and column type.

1. Research Reagent Solutions & Materials Table 2: Essential Materials for Robustness Testing

Item	Function/Description	Example Specifications
HPLC System	Binary or quaternary pump, auto-sampler, column oven, and DAD or MS detector.	Agilent 1290 Infinity III (1300 bar) or equivalent [26].
Analytical Columns	Columns for robustness testing, including primary and equivalent alternatives.	C18, 150 x 4.6 mm, 2.7 μm superficially porous particles [25]. Example: Halo C18 or Accucore C18.
Inert Column (Optional)	For analyzing metal-sensitive compounds; features passivated hardware.	Halo Inert or Raptor Biphenyl Inert column to minimize analyte adsorption [25].
Mobile Phase Buffers	Provides pH control and buffer capacity.	Phosphate or ammonium formate buffers; prepared with HPLC-grade water.
Organic Modifiers	Mobile phase component for gradient elution.	Acetonitrile, Methanol (HPLC grade).
Standard Mixture	Contains all target analytes and internal standards.	Prepared in appropriate mobile phase or solvent.

2. Experimental Workflow The following diagram visualizes the workflow for a robustness screening study:

3. Procedure

• Step 1: Define the Nominal Method. Establish the baseline HPLC method with fixed parameters (column, gradient, detection).
• Step 2: Select Factors and Ranges. Choose factors to test (e.g., Flow Rate: 1.0 ± 0.1 mL/min; pH: 3.0 ± 0.1; Temperature: 40 ± 5°C) and select an appropriate experimental design.
• Step 3: Prepare Solutions. Prepare mobile phases at the high, low, and nominal pH values. Ensure consistent buffer concentration and organic modifier ratio.
• Step 4: Execute Runs. Program the experimental runs according to the design matrix and execute them in a randomized order to minimize bias.
• Step 5: Data Acquisition. For each run, record the retention time (Rt), peak area, and resolution (Rs) between critical pairs for all analytes.

4. Data Analysis

• Step 1: Calculate Effects. For each parameter and each response, calculate the main effect: (Average Response at High Level) - (Average Response at Low Level).
• Step 2: Statistical Significance. Use an ANOVA or half-normal probability plot to determine which effects are statistically significant.
• Step 3: Establish Control Limits. Based on the observed variation, set acceptable ranges for each critical parameter to ensure system suitability.

Protocol 2: Investigating Column Stability at High Temperature

This protocol assesses the stability of a column under elevated temperature conditions, which is relevant for High-Temperature LC (HTLC) method development in food analysis.

1. Workflow for Temperature Stability Assessment

2. Procedure

• Step 1: Baseline Characterization. On a new column, equilibrate at a standard temperature (e.g., 40°C). Inject a standard mixture and record the plate number (N), peak asymmetry (As), and retention time (Rt) for a key analyte.
• Step 2: Thermal Stress Test. Increase the column oven temperature to the target high temperature (e.g., 90°C). Continuously pump mobile phase and periodically inject the standard mixture over an extended period (e.g., 24-48 hours).
• Step 3: Performance Monitoring. For each injection, calculate the key performance metrics (N, As, Rt). Plot these values over time to track performance degradation.
• Step 4: Data Interpretation. A significant drop (>20%) in plate count or a pronounced increase in peak tailing indicates that the temperature is degrading the column. Zirconia-based and certain hybrid polymer columns generally offer superior high-temperature stability compared to conventional silica-based phases [23].

Table 3: Guidelines for High-Temperature LC Parameter Selection

Parameter	Standard Condition	High-Temperature Condition	Considerations and Impact
Temperature	30 - 40 °C	90 - 150 °C	Stationary Phase Stability is critical. Zirconia/polymer columns preferred. Analyte degradation is minimized by fast run times [23].
Flow Rate	1.0 mL/min	Can be increased significantly	Viscosity drops at high T, reducing backpressure. Allows use of longer columns or smaller particles for higher efficiency without exceeding pressure limits [23].
Mobile Phase	Acetonitrile/Water	May use pure subcritical water	Organic solvent consumption is reduced or eliminated. Dielectric constant of water decreases at high T, providing elution strength similar to organic solvent mixtures [23].
Pre-heating	Not required	Essential	Thermal mismatch between cold incoming eluent and hot column causes poor reproducibility. Requires a pre-heating coil before the column inlet [23].

Data Interpretation and System Suitability

Establishing Acceptance Criteria

From the robustness study data, establish system suitability test (SST) limits that ensure the method performs reliably. These are often derived from the observed variation in the study. For example:

• Retention Time: ± 2-3% of the nominal value from the robustness study.
• Resolution: Rs between a critical pair of peaks should not fall below 1.8 under any robustness condition.
• Tailing Factor: Should remain below 1.5 for all main peaks.

Case Study: Application in Food Analysis

A practical application involves the detection of alkylresorcinols (ARs) in whole grain products, a marker for whole grain content. A recently published method uses a column temperature of 32°C, a flow rate of 0.5 mL/min, and a post-column derivatization technique for highly sensitive fluorescence detection [27]. A robustness test for this method would systematically vary the temperature (e.g., 30–34°C), flow rate (e.g., 0.45–0.55 mL/min), and the pH of the derivatization buffer to ensure that the sensitive LODs (0.015–0.040 ng/mL) and recovery rates (98.96–107.92%) are maintained under normal operational variations [27].

Systematic parameter selection and robustness testing are not optional, but fundamental components of a rigorous HPLC method development process, especially for food applications with complex matrices and strict regulatory requirements. By employing structured experimental designs—such as Plackett-Burman or fractional factorial designs—to probe the effects of flow rate, pH, temperature, and column variations, researchers can build quality and reliability directly into their methods. This proactive approach identifies critical parameters early, defines robust system suitability criteria, and ensures that the method will perform consistently in different laboratories, on different instruments, and over time, thereby supporting the validity and longevity of scientific research in both academic and industrial settings.

Robustness is defined as the capacity of an analytical procedure to remain unaffected by small, deliberate variations in method parameters [21]. In High-Performance Liquid Chromatography (HPLC), this evaluates the method's resilience to changes in operational conditions such as mobile phase pH, flow rate, and column temperature. Establishing robustness provides assurance that a method will perform reliably during routine use in quality control laboratories, a critical requirement for both pharmaceutical and food analysis [3]. A robust method can tolerate typical instrument-to-instrument and analyst-to-analyst variations without compromising the accuracy or precision of results, thereby reducing method transfer failures between laboratories.

The terms "robustness" and "ruggedness" are often used interchangeably but have distinct meanings in analytical chemistry. Robustness refers to a method's stability against small, intentional changes to internal parameters specified in the procedure (e.g., mobile phase composition, pH, flow rate, temperature). Ruggedness, increasingly referred to as intermediate precision, addresses external variations such as different laboratories, analysts, instruments, and days [3]. For regulatory compliance, demonstrating robustness has become an essential component of method validation, confirming that a method maintains its performance characteristics under normal operational fluctuations [21] [28].

Core Concepts of Experimental Design

The Rationale for Multivariate Approaches

Traditional univariate optimization, where one parameter is changed while others remain constant, is inefficient and often fails to detect interactions between factors [3] [29]. Multivariate experimental designs simultaneously vary multiple parameters, enabling researchers to not only assess individual factor effects but also identify how factors interact to affect chromatographic responses. This approach provides a more comprehensive understanding of the method's operational limits while significantly reducing the number of experiments required [29].

Experimental designs are particularly valuable in HPLC method development for food applications, where methods must reliably quantify analytes across diverse sample matrices. These designs systematically explore the method operable design region (MODR) – the multidimensional space where method performance criteria are consistently met – providing scientific evidence for establishing system suitability parameters and method control strategies [30].

Three primary experimental designs are employed for robustness screening in HPLC methods. The table below compares their key characteristics, advantages, and limitations.

Table 1: Comparison of Experimental Designs for Robustness Testing

Design Type	Number of Runs	Key Features	Best Applications	Main Limitations
Full Factorial	2k (k = factors)	Evaluates all possible factor combinations; detects all main effects and interactions [3]	Ideal for ≤4 factors; provides complete interaction information [21]	Runs become impractical with high factor numbers (e.g., 9 factors = 512 runs) [3]
Fractional Factorial	2k-p (p = fraction)	Studies main effects and lower-order interactions with fewer runs [31] [3]	Screening 5+ factors; balance between efficiency and information [32]	Effects are aliased (confounded); requires careful fraction selection [3]
Plackett-Burman	Multiples of 4	Highly efficient for screening main effects only [21] [3]	Preliminary screening of many factors (e.g., 7 factors in 12 runs) [33]	Cannot estimate interactions; may miss important factor relationships [21]

Experimental Designs: Principles and Protocols

Full Factorial Design

Principles and Applications

Full factorial design investigates all possible combinations of factors at their designated levels. For a robustness study, factors are typically examined at two levels (high and low), resulting in 2k experiments for k factors [3]. This design is considered the most comprehensive approach for robustness evaluation as it can estimate all main effects and interaction effects between factors [21]. The mathematical model for a two-factor full factorial design can be represented as:

Y = β₀ + β₁A + β₂B + β₁₂AB + ε

Where Y is the response variable, β₀ is the overall mean, β₁ and β₂ are main effects, β₁₂ is the interaction effect, and ε represents random error.

Full factorial designs are particularly valuable in HPLC method development when the number of critical factors is limited (typically four or fewer), providing complete information about the factor effects and their interactions. For instance, this design can effectively evaluate how mobile phase pH and organic modifier percentage interact to affect chromatographic resolution [3].

Protocol for Implementation

Step 1: Factor Selection and Level Definition Identify critical method parameters (typically 2-4) that may influence HPLC performance. Common factors include:

• Mobile phase pH (±0.1-0.2 units)
• Flow rate (±0.1 mL/min)
• Column temperature (±2-5°C)
• Wavelength detection (±2-3 nm)
• Organic modifier composition (±2-5%)

Establish appropriate high (+) and low (-) levels based on expected operational variations in routine laboratories [3] [34].

Step 2: Experimental Setup and Run Order For k factors, determine the 2k experimental runs. Randomize the run order to minimize systematic bias. For example, with 3 factors (A, B, C), 8 experiments are required:

Table 2: Full Factorial Design Matrix for 3 Factors

Run	Factor A	Factor B	Factor C
1	-	-	-
2	+	-	-
3	-	+	-
4	+	+	-
5	-	-	+
6	+	-	+
7	-	+	+
8	+	+	+

Step 3: Response Measurement and Data Analysis Execute experiments and record critical responses such as:

• Resolution between critical peak pairs
• Retention time of active compounds
• Tailing factor
• Theoretical plates

Analyze data using Analysis of Variance (ANOVA) to identify significant factors and interactions. Pareto charts and normal probability plots can help visualize significant effects [3] [29].

Figure 1: Full Factorial Design Workflow

Fractional Factorial Design

Principles and Applications

Fractional factorial designs (FFD) constitute a fraction (½, ¼, etc.) of the full factorial design, significantly reducing the number of experimental runs while still providing information on main effects and lower-order interactions [3] [32]. The design resolution indicates the confounding pattern: Resolution III designs confound main effects with two-factor interactions, Resolution IV designs confound two-factor interactions with each other, and Resolution V designs confound two-factor interactions with three-factor interactions [3].

These designs are particularly valuable when dealing with 5 or more factors where full factorial designs become prohibitively large [21] [31]. FFDs have been successfully applied in pharmaceutical analysis, such as in the development of a robust HPLC method for simultaneous determination of xipamide and valsartan in human plasma, where it efficiently screened four independent factors (pH, flow rate, detection wavelength, and methanol percentage) with a minimal number of experiments [31].

Protocol for Implementation

Step 1: Design Resolution Selection Select an appropriate resolution based on the number of factors and the effects of interest. For robustness screening of 5-8 factors, Resolution IV or V designs are typically recommended to avoid confounding main effects with two-factor interactions [3] [32].

Step 2: Design Matrix Generation Create a 2k-p fractional factorial design, where k is the number of factors and p determines the fraction size. For example, a 26-2 FFD (16 runs) can effectively screen 6 factors for SNEDDS (Self-Nanoemulsifying Drug Delivery Systems) formulation composition selection [32].

Step 3: Aliasing Structure Evaluation Review the aliasing (confounding) structure to understand which effects are correlated. If necessary, augment the design with additional runs to de-alias critical effects [3].

Step 4: Data Analysis and Interpretation Analyze data using multiple linear regression. Effects plots and half-normal probability plots help identify significant factors. Consider conducting confirmation runs at the nominal conditions to verify predictions [32].

Table 3: Fractional Factorial Applications in Pharmaceutical Analysis

Application	Design Type	Factors Screened	Key Outcomes	Reference
Xipamide & Valsartan HPLC	Resolution IV FFD	pH, flow rate, wavelength, %MeOH	Identified flow rate and %MeOH as significant	[31]
SNEDDS Formulation	26-2 FFD	Oil, surfactant, co-surfactant types and ratios	Selected optimal components with 16 runs vs 64 in full factorial	[32]
NSAIDs HPLC Separation	FFD with CCD	Organic modifier (Φ), pH, temperature (T)	Reduced experimental space for optimization	[29]

Plackett-Burman Design

Principles and Applications

Plackett-Burman designs are highly efficient screening designs that require a number of runs equal to multiples of 4 (e.g., 8, 12, 16, 20) rather than powers of 2 [3]. These designs are particularly valuable for investigating up to N-1 factors in N runs, making them extremely efficient for preliminary screening of many factors when only main effects are of interest [21] [33].

The designs are based on Hadamard matrices and assume that interactions are negligible compared to main effects. Plackett-Burman designs have been widely employed in robustness testing of HPLC methods, including the development of a stability-indicating method for simultaneous determination of clorsulon and ivermectin, where it efficiently assessed the impact of multiple chromatographic factors on system performance [33].

Protocol for Implementation

Step 1: Factor and Run Number Selection Select factors for investigation and determine the appropriate number of runs (multiple of 4). A 12-run Plackett-Burman design can screen up to 11 factors, making it exceptionally efficient for initial robustness assessment [3].

Step 2: Experimental Matrix Construction Construct the design matrix using standard Plackett-Burman templates. Each column represents a factor, and each row represents an experimental run with factors set at high (+) or low (-) levels [3]:

Table 4: Plackett-Burman Design Matrix Template (8 runs for 7 factors)

Run	F1	F2	F3	F4	F5	F6	F7
1	+	+	+	-	+	-	-
2	-	+	+	+	-	+	-
3	-	-	+	+	+	-	+
4	+	-	-	+	+	+	-
5	-	+	-	-	+	+	+
6	+	-	+	-	-	+	+
7	+	+	-	+	-	-	+
8	-	-	-	-	-	-	-

Step 3: Experiment Execution and Response Measurement Execute experiments in randomized order and record relevant chromatographic responses (retention time, resolution, peak area, etc.).

Step 4: Statistical Analysis Calculate the effect of each factor using the following formula:

Effect = (ΣY⁺ - ΣY⁻) / (N/2)

Where ΣY⁺ is the sum of responses when the factor is at high level, ΣY⁻ is the sum of responses when the factor is at low level, and N is the total number of runs.

Perform t-tests to determine the statistical significance of each effect [33].

Figure 2: Plackett-Burman Design Workflow

Applications in Food and Pharmaceutical Analysis

Case Studies in Food Analysis

Experimental designs have been successfully implemented in food analysis to ensure method robustness across diverse sample matrices. In one application, a validated HPLC method was developed for simultaneous determination of four preservatives (benzoic acid, sorbic acid, methylparaben, and propylparaben) in various consumer goods, including fruit juices, ketchup, cakes, and herbal products. Robustness testing confirmed method reliability across different matrices, which is crucial for accurate health risk assessments [28].

Another study employed full factorial design to develop a green HPLC method for analyzing curcuminoids in Curcuma longa extracts, tablets, and capsules. Critical method parameters (mobile phase ratio, pH, and column temperature) were systematically varied to establish robustness, with Monte Carlo simulation used to evaluate the method's operability design region [30].

Case Studies in Pharmaceutical Analysis

In pharmaceutical development, a Quality by Design (QbD) approach utilizing fractional factorial design was applied to develop and validate an RP-HPLC method for simultaneous estimation of xipamide and valsartan in human plasma. The design efficiently screened four independent factors (pH, flow rate, detection wavelength, and % of MeOH), with ANOVA confirming that only flow rate and % of MeOH were statistically significant. This approach ensured method predictability and robustness according to FDA guidelines [31].

Similarly, a Plackett-Burman design was employed in the development of a validated stability-indicating RP-HPLC method for simultaneous determination of clorsulon and ivermectin. The design assessed robustness by evaluating multiple factors simultaneously, defining factors affecting system performance, and determining nonsignificant intervals for the significant factors [33].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 5: Essential Research Reagents and Materials for HPLC Robustness Studies

Category	Specific Examples	Function in Robustness Testing
HPLC Instrumentation	HPLC with DAD/UV detection, quaternary pumps, column oven [31] [28]	Provides controlled variation of operational parameters (flow, temperature, wavelength)
Chromatographic Columns	C8, C18, phenyl-modified silica columns (e.g., BDS Hypersil C8, ZORBAX SB phenyl) [31] [33]	Evaluating column selectivity variations; different column lots assess performance consistency
Mobile Phase Components	HPLC-grade methanol, acetonitrile, ethanol; buffer salts (KH₂PO₄, NaH₂PO₄); pH modifiers (ortho-phosphoric acid) [31] [28] [34]	Assessing method sensitivity to mobile phase composition and pH variations
Reference Standards	Certified reference materials of target analytes (e.g., benzoic acid, sorbic acid, methylparaben) [28]	Ensuring accurate quantification during method parameter variations
Software Tools	Experimental design software (Minitab, Design-Expert); chromatographic data systems [31] [34]	Designing experiments, randomizing runs, and analyzing multivariate data

Integration with Green Analytical Chemistry Principles

The application of experimental designs aligns with Green Analytical Chemistry (GAC) principles by minimizing solvent consumption and waste generation through reduced experimentation [13] [34]. Factorial designs enable researchers to establish robust methods while systematically evaluating eco-friendly alternatives to traditional solvents, such as replacing acetonitrile with ethanol in mobile phases [34].

Greenness assessment tools, including the Analytical Eco-Scale, Green Analytical Procedure Index (GAPI), and AGREE metric, provide quantitative evaluations of the environmental impact of analytical methods [13]. These tools consider factors such as solvent toxicity, energy consumption, and waste generation, allowing researchers to make informed decisions that balance analytical performance with environmental sustainability [34]. The integration of experimental designs with green chemistry principles represents a comprehensive approach to developing environmentally responsible yet robust analytical methods for food and pharmaceutical analysis.

Aflatoxins, potent mycotoxins produced primarily by Aspergillus flavus and Aspergillus parasiticus, represent a significant food safety hazard due to their carcinogenic, hepatotoxic, and immunosuppressive effects [35] [36]. Their presence in meat products can occur through direct mold growth on processed meats or indirectly via the transfer from contaminated feed consumed by animals [37] [38]. Robustness testing of High-Performance Liquid Chromatography (HPLC) methods is a critical validation component, ensuring that aflatoxin quantification remains unaffected by small, deliberate variations in method parameters [3] [39]. This study outlines a structured protocol for assessing the robustness of an HPLC method for quantifying Aflatoxin B1 (AFB1) in processed meat products, providing a framework applicable to food safety research and regulatory analysis.

Literature Review: Aflatoxin Analysis in Meat Matrices

The analysis of aflatoxins in complex matrices like meat presents specific challenges. A 2021 study investigating Egyptian processed beef products (basterma, sausage, minced meat) successfully employed an HPLC method with fluorescence detection, finding AFB1 in 15% of basterma samples [37]. This highlights the practical relevance and need for reliable detection methods. Sample preparation typically involved homogenization with acetonitrile:water (60:40, v/v) followed by liquid-liquid extraction and clean-up, achieving sensitive detection [37]. Another study on milk, eggs, and meat used HPLC with both fluorescent and UV detectors, reporting recoveries between 92% and 109%, demonstrating the feasibility of achieving accurate results in animal product matrices [40].

Experimental Design for Robustness Testing

Definition and Objective

Robustness is defined as "a measure of [an analytical procedure's] capacity to remain unaffected by small but deliberate variations in procedural parameters listed in the documentation" [3]. The objective of this study is to systematically evaluate the impact of key HPLC parameters on the quantification of AFB1 in spiked sausage samples and to establish method tolerances that ensure reliability during routine use.

Regulatory Context

While robustness is not a strict validation parameter in some guidelines, it is investigated during method development and is expected by regulatory agencies like the FDA and EMA [3] [39]. The International Conference on Harmonization (ICH) guideline Q2(R1) provides a foundational framework for this assessment [41].

Experimental Workflow

The following workflow outlines the sequential stages for conducting robustness testing, from initial preparation to final decision-making.

Materials and Methods

Research Reagent Solutions and Essential Materials

The following table details key reagents, materials, and equipment required for the robustness testing protocol.

Table 1: Essential Research Reagents and Materials for Aflatoxin Robustness Testing

Item	Specification/Function
Aflatoxin B1 Certified Reference Standard	Primary standard for calibration and spiking; purity ≥ 98% [37].
Acetonitrile (HPLC Grade)	Organic solvent for mobile phase and sample extraction [37] [38].
Methanol (HPLC Grade)	Organic solvent for mobile phase preparation and dilution [38].
Water (HPLC Grade)	Aqueous component of mobile phase and solvents.
Orthophosphoric Acid / Formic Acid	Mobile phase modifier to control pH and improve peak shape [41] [37].
C18 Reverse-Phase HPLC Column	Stationary phase for analytical separation; 150 x 4.6 mm, 3 μm [42].
Solid-Phase Extraction (SPE) Cartridges	For sample clean-up (e.g., Cyanopropyl, Immunoaffinity) [40] [38].
HPLC System with FLD	Instrumentation: quaternary pump, autosampler, column oven, fluorescence detector [37].
Photochemical Reactor for Derivatization	Post-column derivatization to enhance AFB1 fluorescence signal [38].

Sample Preparation Protocol

• Homogenization: Aseptically mince 25 g of the processed meat sample (e.g., sausage) [37].
• Extraction: Homogenize the sample with 100 mL of acetonitrile:water (60:40, v/v) using a high-speed blender for 2 minutes [37].
• Filtration: Pass the homogenate through fast-filtering Whatman No. 1 filter paper.
• Clean-up: Purify a 4 mL aliquot of the filtrate using a Cyanopropyl (CN) Solid-Phase Extraction cartridge or an immunoaffinity column specific for aflatoxins [40] [37].
• Evaporation and Reconstitution: Gently evaporate the eluent to dryness under a nitrogen stream and reconstitute the residue in 1 mL of methanol:water (50:50, v/v) for HPLC analysis [37].

Base HPLC Conditions

• Column: C18, 150 x 4.6 mm, 3 μm [42]
• Mobile Phase: Water:MeOH (50:50, v/v) with 1 mL/L orthophosphoric acid (isocratic) [41]
• Flow Rate: 1.0 mL/min
• Column Temperature: 35 °C [41]
• Injection Volume: 10 μL
• Detection: Fluorescence Detector (FLD); typical excitation/emission wavelengths for AFB1: 365 nm / 435 nm [40] [38]
• Post-Column Derivatization: Photochemical (PHRED) to enhance aflatoxin signal [38]

Robustness Testing Parameters and Experimental Design

A fractional factorial design is recommended to efficiently investigate multiple factors simultaneously. This study will examine five critical parameters, each varied at two levels around the nominal value.

Table 2: Robustness Testing Parameters and Their Varied Levels

Parameter	Nominal Value	Low Level (-)	High Level (+)
Mobile Phase MeOH %	50%	48%	52%
Mobile Phase pH	3.0	2.8	3.2
Flow Rate (mL/min)	1.0	0.9	1.1
Column Temperature (°C)	35	33	37
Detection Wavelength (nm)*	365/435	-2 nm	+2 nm

*Wavelength variation applies to both Ex and Em, maintaining the same difference.

Results and Data Analysis

Data Collection and Key Performance Indicators

For each experimental run in the design, the following critical responses will be measured:

• Retention Time (t_R) of the AFB1 peak.
• Peak Area for quantification.
• Theoretical Plates (N) representing column efficiency.
• Peak Tailing Factor (T_f)
• Resolution (R_s) from the closest eluting interferent peak.

Data Interpretation and Statistical Analysis

The effects of the parameter variations on the responses are analyzed to determine the method's robustness. The following table provides an example of acceptance criteria and how to interpret the observed effects.

Table 3: Acceptance Criteria and Interpretation of Parameter Effects

Response Variable	Acceptance Criterion	Interpretation of Significant Effect
Retention Time (tR)	RSD ≤ 2% across all runs	Indicates sensitivity to parameters affecting retention (e.g., %MeOH, pH, T).
Peak Area	RSD ≤ 5% across all runs	Suggests that detection or injection is influenced by the varied parameter.
Theoretical Plates (N)	N ≥ 2000	A significant drop indicates reduced separation efficiency.
Tailing Factor (Tf)	Tf ≤ 2.0	A significant increase suggests peak broadening due to secondary interactions.
Resolution (Rs)	Rs ≥ 1.5 from closest peak [42]	A drop below 1.5 indicates potential for co-elution, risking inaccurate quantification.

Data analysis should employ statistical methods such as Analysis of Variance (ANOVA) to objectively identify which parameters have a statistically significant effect on the responses. The goal is to establish the "acceptable range" for each parameter—the range over which variations do not lead to a statistically significant or practically relevant degradation of the analytical results [3].

A systematic robustness assessment is indispensable for developing a reliable HPLC method for aflatoxin quantification in complex meat matrices. By proactively identifying critical parameters and their acceptable operational ranges, this study provides a protocol that ensures method resilience during routine application and transfer between laboratories.

Based on this study, the following recommendations are made:

• For Method Documentation: The final method should explicitly state the controlled ranges for critical parameters identified in this robustness study (e.g., "Mobile phase MeOH content: 48% - 52%").
• For System Suitability: Criteria for system suitability tests should be derived from the worst-case conditions observed during robustness testing to guarantee performance in every analytical run.
• For Laboratory Practice: Analysts should be trained on the importance of strictly controlling parameters like mobile phase pH and column temperature, which are often key sources of variability.

This rigorous approach to robustness testing strengthens the overall validity of aflatoxin data, supports regulatory compliance, and ultimately enhances the safety of the food supply.

The incorporation of nicotinamide mononucleotide (NMN) into pet foods represents a growing trend aimed at enhancing pet health and longevity. As a key precursor to nicotinamide adenine dinucleotide (NAD+), NMN supplementation is associated with potential benefits such as improved cellular energy metabolism, vitality, and immune function in companion animals [43] [44]. However, the absence of standardized analytical methods for quantifying NMN in complex pet food matrices presents significant challenges for quality control, regulatory compliance, and ensuring product efficacy [43]. This case study details the development and validation of a robust high-performance liquid chromatography (HPLC) method specifically designed for the accurate quantification of NMN in commercial pet foods, framed within the broader context of ensuring method reliability for food applications.

Method Development Strategy

Chromatographic Optimization

The initial method development focused on achieving optimal separation of NMN from potential matrix interferences in pet food samples. Researchers evaluated multiple column chemistries, including conventional C18 and various hydrophilic interaction chromatography (HILIC) columns [43]. The final method utilized a PC HILIC column (250 mm × 4.6 mm, 5 μm particle size, 100 Å pore size) which demonstrated superior performance for retaining and separating the highly polar NMN molecule. The isocratic mobile phase consisted of 0.1% formic acid in water (Mobile Phase A) and 0.1% formic acid in methanol (Mobile Phase B) at a ratio of 15:85 (v/v) [43]. This optimized condition facilitated efficient chromatography with a flow rate of 1.0 mL/min, column temperature maintained at 35°C, and detection at 235 nm using a photodiode array (PDA) detector [43].

Sample Preparation Protocol

A critical aspect of ensuring method robustness involved optimizing the sample preparation procedure to effectively extract NMN from various pet food formulations while minimizing matrix effects. The developed protocol is as follows [43]:

• Sample Homogenization: Capsule contents were removed from their shells, while granules and tablets were crushed using a small pulverizer. All samples were passed through a 40-mesh sieve to ensure uniform particle size.

• Extraction: An accurately weighed 0.5 g portion of the homogenized sample was transferred to a 50 mL centrifuge tube. Then, 25 mL of a 30% methanol solution was added as the extraction solvent.

• Isolation: The mixture was subjected to ultrasonic extraction in an ice bath for 30 minutes to facilitate efficient NMN release without degradation. The volume was subsequently made up to 50 mL with the 30% methanol solution.

• Clarification: The extract was centrifuged at 10,000 rpm for 5 minutes to separate solid particulates. A 2 mL aliquot of the supernatant was diluted to 25 mL in a volumetric flask.

• Filtration: Prior to HPLC analysis, the diluted extract was filtered through a 0.22 μm organic membrane filter to remove any remaining particulates that could damage the chromatographic system.

Validation of the Analytical Method

The developed HPLC-PDA method was rigorously validated according to established analytical guidelines to demonstrate its reliability for the intended application. The table below summarizes the key validation parameters and their outcomes.

Table 1: Summary of Method Validation Parameters for NMN Quantification in Pet Food

Validation Parameter	Result	Acceptance Criteria
Linearity Range	5–500 μg/mL	R² > 0.99
Limit of Detection (LOD)	1.0 mg/kg	-
Limit of Quantification (LOQ)	2.0 mg/kg	-
Precision (Repeatability)	RSD < 6.0%	RSD < 10%
Accuracy (Spiked Recovery)	97.3–109%	80–120%
Stability	Acceptable	No significant degradation

The method exhibited excellent linearity across the specified concentration range, enabling accurate quantification of NMN at levels expected in fortified products. The low LOD and LOQ confirm the method's sensitivity, which is sufficient for detecting NMN at the concentrations typically found in commercial pet foods. The precision of the method, expressed as relative standard deviation (RSD), was well within acceptable limits, indicating highly reproducible results. Accuracy, determined through spike recovery experiments, showed that NMN could be quantitatively recovered from the pet food matrix with minimal interference [43].

Robustness Testing in Method Reliability

Robustness testing is an essential component of analytical method validation, evaluating a method's capacity to remain unaffected by small, deliberate variations in procedural parameters. For HPLC methods, this typically involves assessing the impact of changes in:

• Mobile Phase Composition: Slight alterations in the organic solvent ratio or buffer pH.
• Flow Rate: Minor deviations from the specified flow rate.
• Column Temperature: Fluctuations in the controlled column oven temperature.
• Different Column Batches/Suppliers: Using equivalent columns from different lots or manufacturers.

While the specific robustness data for the NMN method is not explicitly detailed in the search results, the principles of robustness testing are well-established in analytical chemistry and are implied by the method's successful application to various commercial samples [43]. A robust method ensures that routine analyses produce reliable and consistent data across different laboratories, instruments, and analysts, which is crucial for quality control in pet food manufacturing.

The following workflow diagram illustrates the comprehensive process from method development to the final application for quality control, highlighting the central role of validation and robustness testing.

Essential Research Reagents and Materials

The successful implementation of this analytical method relies on several key reagents and materials. The table below lists these critical components and their specific functions in the analysis.

Table 2: Key Research Reagent Solutions for NMN HPLC Analysis

Reagent/Material	Function/Application	Specifications
NMN Standard	Analytical standard for calibration and identification	Purity ≥ 98% [43]
PC HILIC Column	Stationary phase for chromatographic separation	250 mm × 4.6 mm, 5 μm, 100 Å [43]
Methanol	Mobile phase component and extraction solvent	Chromatographic purity [43]
Formic Acid	Mobile phase modifier	Chromatographic purity, used at 0.1% (v/v) [43]
Ultrapure Water	Mobile phase and solution preparation	Purified via dedicated water system [43]

Analysis of Commercial Pet Food Samples

The validated method was applied to analyze NMN content in five commercial pet food supplements, including three capsule samples purchased from online platforms and two self-developed samples (granules and tablets) provided by manufacturers [43]. The results demonstrated that most tested commercial samples complied with their labeled claims, thereby verifying the method's practical applicability for real-world quality control and regulatory monitoring [43]. This application is crucial for ensuring that pets receive the intended dosage of NMN for potential health benefits and for protecting consumers from fraudulent products.

This case study presents a simple, efficient, and validated HPLC-PDA method for the quantification of NMN in fortified pet foods. The method demonstrates excellent accuracy, precision, and sensitivity, making it suitable for routine quality control in manufacturing and regulatory compliance testing. The rigorous validation and inherent robustness of the method provide a reliable tool for ensuring the quality and efficacy of NMN-containing pet food products. This work fills a critical technical gap in the industry and supports broader efforts to promote pet health through nutritional science. Future work could focus on transferring the method to LC-MS platforms for even greater sensitivity and selectivity, particularly for complex matrices [45].

Establishing System Suitability Criteria from Robustness Data

In the development of High-Performance Liquid Chromatography (HPLC) methods for food and pharmaceutical analysis, robustness testing serves as a critical foundation for establishing reliable system suitability criteria. The International Conference on Harmonization (ICH) and United States Pharmacopeia (USP) define robustness as "a measure of the analytical procedure's capacity to remain unaffected by small, but deliberate variations in method parameters," providing an indication of its reliability during normal usage [3]. For researchers and scientists, systematically deriving system suitability parameters from robustness studies ensures that methods maintain performance standards even when minor operational variations occur, thereby guaranteeing the analytical integrity of results for complex food matrices and pharmaceutical formulations.

This application note provides detailed protocols for conducting robustness studies and translating the resulting data into scientifically sound system suitability criteria, framed within the context of HPLC method validation for food applications.

Theoretical Foundation: Connecting Robustness to System Suitability

Defining Key Concepts

Robustness represents an internal method characteristic that evaluates the impact of deliberate, controlled variations in methodological parameters written into the procedure [3]. In practice, this involves testing how small changes in parameters such as mobile phase pH, column temperature, or flow rate affect chromatographic outcomes.

System Suitability Testing (SST) serves as a quality control check to verify that the complete analytical system—including instrument, reagents, column, and analyst—is functioning adequately at the time of analysis [46]. According to USP guidelines, key SST parameters include resolution, precision (Relative Standard Deviation, RSD), and tailing factor [46].

The crucial relationship between these concepts lies in utilizing robustness data to establish science-based SST limits that ensure method performance despite normal laboratory variations, rather than relying on arbitrary or generic criteria.

Regulatory Framework

ICH guideline Q2(R1) and USP Chapter <1225> provide the fundamental framework for analytical method validation, with robustness being an expected though not always strictly required parameter [3] [47]. USP Chapter <621> offers flexibility for method adjustments without full revalidation, provided system suitability requirements are still met [46]. This regulatory landscape underscores the importance of establishing appropriate SST criteria derived from robustness assessment to maintain method validity throughout its lifecycle.

Experimental Design for Robustness Assessment

Parameter Selection and Delimitation

A scientifically sound robustness study begins with identifying critical method parameters likely to affect chromatographic performance. Based on the intended use for food additive analysis, key factors typically include:

• Mobile phase composition (organic modifier ratio, buffer concentration)
• pH of aqueous phase
• Column temperature
• Flow rate
• Detection wavelength [3] [19]

Parameter variations should reflect realistic laboratory deviations (±0.1 pH units, ±5% flow rate, ±2°C temperature, etc.) rather than extreme values that would invalidate the method [3].

Figure 1: Workflow for robustness study design and implementation

Experimental Design Approaches

For robustness testing, screening designs are most appropriate to efficiently identify factors significantly affecting method performance [3]. The three primary design options include:

• Full factorial designs: Test all possible combinations of factors at two levels (high/low). Suitable for ≤4 factors (2^k runs) [3]
• Fractional factorial designs: Examine a carefully chosen subset of factor combinations. Ideal for 5+ factors (2^k-p runs) [3]
• Plackett-Burman designs: Highly economical designs in multiples of four runs. Optimal when only main effects are of interest [3]

For example, a study developing an HPLC-DAD method for seven food additives and caffeine in powdered drinks employed a Box-Behnken Design with three factors (% methanol at gradient start, % methanol at gradient end, and pH) to efficiently optimize separation conditions [19].

Protocol: Conducting a Robustness Study for Food Additive HPLC

Materials and Reagents

Table 1: Essential research reagents and materials for robustness assessment

Item	Specification	Function/Purpose
HPLC System	Binary pump, DAD/UV detector, autosampler, column thermostat	Chromatographic separation and detection
Analytical Column	C18, 150-250 mm × 4.6 mm, 3-5 μm particles	Stationary phase for compound separation
Mobile Phase A	Phosphate buffer (appropriate molarity and pH)	Aqueous component for reverse-phase separation
Mobile Phase B	HPLC-grade methanol or acetonitrile	Organic modifier for elution strength control
Reference Standards	Certified target analytes (e.g., sweeteners, preservatives, colorants)	System qualification and quantitative calibration
Placebo/Blank Matrix	Food matrix without target analytes	Specificity assessment and interference check

Experimental Procedure

• Prepare test solutions: Create a mixture containing all target analytes at representative concentrations. For food additive methods, include acesulfame potassium, benzoic acid, sorbic acid, sodium saccharin, tartrazine, sunset yellow, caffeine, and aspartame at typical concentration ranges [19].

• Establish baseline conditions: Based on optimized method parameters from development. For example: C18 column (150 mm × 4.6 mm, 5 μm), mobile phase A (phosphate buffer), mobile phase B (methanol), gradient elution with initial 8.5% B to 90% B over 16 min, pH 6.7, flow rate 1.0 mL/min, column temperature 30°C, detection at 210 nm [19].

• Execute experimental design: Systematically vary parameters according to selected design. For each experimental run, inject the test solution in triplicate.

• Record chromatographic responses: For each run, document critical performance indicators including retention times, peak areas, resolution between critical pairs, tailing factors, and theoretical plates.

• Statistical analysis: Calculate mean, standard deviation, and relative standard deviation (RSD) for replicated injections. For designed experiments, perform analysis of variance (ANOVA) to identify significant effects.

Data Analysis: Translating Robustness Data to SST Criteria

Statistical Treatment

Analysis of robustness data should identify which parameter variations cause statistically significant effects on critical chromatographic responses. Effects exceeding predetermined thresholds (e.g., >2% RSD for retention time, >0.2 min resolution change) indicate method vulnerabilities [3] [47].

For factorial designs, calculate the main effects of each parameter and their interactions. Establish acceptable ranges for each parameter where system suitability criteria remain met.

Establishing Science-Based System Suitability Criteria

Table 2: Deriving system suitability criteria from robustness data

SST Parameter	Derivation from Robustness Data	Typical Acceptance Criteria	Food Application Example
Resolution (Rs)	Minimum resolution observed between critical pair across all robustness conditions	Rs ≥ 1.5 between any two peaks [46]	Resolution between aspartame and caffeine maintained >2.0 despite pH variations ±0.2 units [19]
Tailing Factor (Tf)	Maximum tailing factor observed across all robustness variations	Tf ≤ 2.0 [46]	Peak symmetry remained <1.8 despite temperature variations ±3°C [19]
Precision (RSD)	Worst-case relative standard deviation for peak areas/retention times across robustness study	RSD ≤ 2.0% for replicate injections [46] [47]	Area RSD <1.5% maintained despite mobile phase composition variations ±2% [19]
Theoretical Plates (N)	Minimum plate count observed across all experimental conditions	N > 2000 per column	Plate count for benzoic acid >5000 despite flow rate variations ±0.1 mL/min

Case Study: Implementing the Protocol for Powdered Drink Analysis

A practical application of this protocol was demonstrated in the development of an HPLC-DAD method for simultaneous determination of seven food additives and caffeine in powdered drinks [19]. The robustness study employed a Box-Behnken Design with three factors and three center points (15 runs total) to evaluate the effects of:

• x1: Mobile phase composition at gradient start (0-10% methanol)
• x2: Mobile phase composition at gradient end (60-100% methanol)
• x3: pH of mobile phase (3-7)

The experimental data revealed that pH was the most critical parameter affecting resolution of critical peak pairs, particularly for aspartame and caffeine. However, across all robustness variations, the method maintained resolution >1.5, analysis time <16 minutes, and peak symmetry within acceptable limits [19].

From this robustness data, the following system suitability criteria were established:

• Resolution between aspartame and caffeine: Rs ≥ 1.5
• Tailing factor for all peaks: T ≤ 2.0
• RSD for replicate injections: ≤2.0%
• Theoretical plates for benzoic acid: ≥5000

These criteria ensured method reliability when transferred to quality control laboratories for routine analysis of commercial powdered drink products.

Systematically deriving system suitability criteria from robustness data provides a science-based foundation for ensuring HPLC method reliability in food and pharmaceutical applications. The experimental protocols outlined in this application note enable researchers to identify method vulnerabilities, establish meaningful acceptance criteria, and ultimately enhance data quality and regulatory compliance. By implementing this approach, laboratories can confidently transfer methods while maintaining analytical integrity despite normal operational variations encountered in routine practice.

Navigating Analytical Challenges: Proactive Troubleshooting and Optimization of HPLC Methods

Robustness testing is a critical, yet often underestimated, component of analytical method validation for High-Performance Liquid Chromatography (HPLC) in food and pharmaceutical research. Defined as "a measure of its capacity to remain unaffected by small, but deliberate variations in method parameters" [4], robustness provides an essential indication of a method's reliability during normal usage. Within the framework of a broader thesis on HPLC method development for food applications, this application note addresses two pervasive challenges: the management of complex parameter interactions and the constraints imposed by limited development timelines. Unfortunately, as noted by trainers in the field, mistakes in robustness assessment are "all too common" [48]. This document provides detailed protocols to systematically identify and control critical method parameters, ensuring the generation of reliable data that complies with regulatory expectations for food and pharmaceutical analysis.

Common Pitfalls and Systematic Management

Pitfall 1: Investigating Robustness Too Late in the Method Lifecycle

A fundamental mistake is postponing robustness evaluation until the formal method validation stage. If robustness is investigated for the first time during validation, there is a significant risk that the method may be found not to be robust. Any subsequent modifications to improve robustness can invalidate other validation experiments, as they are no longer representative of the final method [48]. This reactive approach is inefficient and costly.

Protocol: Proactive Robustness Integration

• Action: Investigate robustness systematically prior to the execution of the formal validation protocol.
• Tool: Employ a dedicated robustness protocol during the final stages of method development.
• Benefit: This allows for the identification and resolution of robustness issues before full validation commences. The nature of the problems will dictate the solution, which may range from more precise wording in the written method to adjustments of critical method parameters [48].

Pitfall 2: Neglecting Critical Parameters and Their Interactions

A frequent error is the failure to investigate the right factors, often due to an over-reliance on numerical instrument parameters while ignoring sample preparation and other manual steps where robustness problems frequently occur [48]. Furthermore, testing factors in isolation fails to account for parameter interactions, which can have a significant impact on method performance.

Protocol: Risk-Based Parameter Selection

• Action 1: Conduct a comprehensive review of all method steps, including sample preparation, to identify factors whose minor variations could influence the results. The most important factors are often those that were adjusted during method development [48].
• Action 2: Prioritize factors using a risk assessment matrix. Consider both operational parameters (e.g., from the method description) and environmental factors (e.g., not explicitly specified but potentially variable) [4].
• Action 3: Evaluate these factors using a structured experimental design (DoE) that can efficiently uncover interactions, rather than a one-factor-at-a-time (OFAT) approach [4] [49].

Pitfall 3: Failing to Utilize Robustness Data Effectively

Often, robustness data is presented in a validation report without meaningful discussion or is not shared with the analysts who routinely use the method. This "tick-box" approach complies with regulations but fails to leverage the scientific data to improve method control and facilitate smoother method transfer [48].

Protocol: Knowledge Management and Control Strategy

• Action 1: Mandate a detailed discussion of robustness findings in the validation report. This discussion should interpret the data and state its significance for routine use.
• Action 2: Use the results to define evidence-based System Suitability Test (SST) limits. The ICH guidelines state that one consequence of robustness evaluation should be the establishment of system suitability parameters to ensure the validity of the analytical procedure is maintained whenever used [4].
• Action 3: Ensure robustness data is a key input for method transfer and risk assessment for technology transfer to other laboratories [48].

Experimental Protocols for Comprehensive Robustness Assessment

Protocol 1: Risk Assessment and Parameter Selection

Objective: To identify and prioritize method parameters for inclusion in a robustness study. Methodology:

• Deconstruct the Method: List every step of the analytical procedure, from sample weighing and preparation to instrumental analysis and data processing.
• Identify Factors: For each step, identify all variable parameters. Categorize them as quantitative (e.g., pH, temperature, flow rate), qualitative (e.g., column brand, solvent supplier), or mixture-related (e.g., mobile phase composition) [4].
• Risk Analysis: Use a risk matrix to score each parameter based on its potential impact on critical method attributes (e.g., resolution, retention time, peak area) and the probability of its variation during routine use.
• Select Factors: Prioritize high-risk parameters for experimental investigation. A subject matter expert (SME) should be consulted to finalize the selection [48].

Protocol 2: Experimental Design and Execution

Objective: To efficiently evaluate the main effects of selected parameters and their interactions. Methodology:

• Define Ranges: For each selected factor, define a "high" and "low" level that represents a small but deliberate variation around the nominal value, slightly exceeding expected routine fluctuations [4].
• Select Experimental Design:
  • For screening 5 or more factors, a Plackett-Burman design is highly efficient for estimating main effects [4].
  • For a more detailed study of fewer factors (e.g., 3-5), a fractional factorial design (e.g., a 2^(4-1) design) can also estimate main effects and some two-factor interactions [4].
• Execute Experiments: Perform the trials in a randomized order to minimize the impact of uncontrolled variables. Use aliquots from a single, homogeneous sample and standard solution to ensure that observed variations are due to the parameter changes and not sample heterogeneity [4].
• Define Responses: Measure responses that describe both quantitative outcomes (e.g., assay content, peak area) and system suitability parameters (e.g., resolution, tailing factor, capacity factor) [4].

The following workflow outlines the systematic approach to a robustness study:

Quantitative Data Analysis and Effect Calculation

Objective: To statistically analyze the experimental data and identify significant effects. Methodology:

• Calculate Effects: For each factor (X) and each response (Y), calculate the effect using the equation: Effect (Eₓ) = [ΣY(+)/N] - [ΣY(-)/N] where ΣY(+) is the sum of responses where the factor is at the high level, ΣY(-) is the sum at the low level, and N is the number of experiments at each level [4].
• Statistical/Graphical Analysis: Use half-normal probability plots or Pareto charts to visually identify effects that stand out from the noise. Statistical t-tests can also be used to determine the significance of each effect [4].
• Set SST Limits: Based on the observed variations in critical responses like resolution under the tested conditions, set justified, evidence-based system suitability limits to control the method during routine use [4].

The Scientist's Toolkit: Essential Research Reagent Solutions

The following table details key materials and reagents critical for successful robustness testing of HPLC methods in food and pharmaceutical applications.

Table 1: Key Research Reagent Solutions for HPLC Robustness Testing

Item	Function in Robustness Assessment	Application Notes
Chromatography Columns (Different batches/lots)	Assess column-to-column reproducibility, a major source of variability.	Strictly speaking, this falls under intermediate precision, but it is logically investigated as part of robustness [48].
pH Buffers	Evaluate method sensitivity to mobile phase pH variation, a critical factor for ionizable analytes.	A common quantitative factor to test in robustness studies [4].
HPLC-Grade Solvents (from different suppliers or lots)	Determine the impact of solvent purity and composition variability on retention time and peak shape.	Mobile phase composition is a key mixture factor to evaluate [4].
Reference Standards (API and impurities)	Used in "cocktail" solutions to evaluate specificity, resolution, and quantitative accuracy under varied conditions.	Essential for assessing separation quality during robustness testing [47].
Placebo/Blank Matrix	Verify the absence of interference from excipients (pharmaceuticals) or food matrix components under all tested conditions.	Critical for demonstrating method specificity as part of robustness [47].
Forced Degradation Samples	Provide samples with degradation products to challenge method specificity under deliberate parameter variations.	Confirms the method remains stability-indicating across the robustness space [47].

Effectively managing the pitfalls of robustness assessment requires a proactive, systematic approach grounded in sound science. By integrating robustness studies at the end of method development, employing risk-based experimental designs to uncover parameter interactions, and leveraging the resulting data to establish meaningful control strategies, researchers can ensure their HPLC methods are reliable, reproducible, and fit-for-purpose. This disciplined practice is indispensable for advancing robust analytical methods in both food and pharmaceutical research, ultimately safeguarding product quality and consumer safety.

Matrix effects represent a significant challenge in the quantitative analysis of complex food samples using High-Performance Liquid Chromatography (HPLC), particularly when coupled with mass spectrometry (MS) detection. The sample matrix is conventionally defined as the portion of the sample that is not the analyte—effectively, most of the sample [50]. In analytical chemistry, matrix effect is defined as "the combined effects of all components of the sample other than the analyte on the measurement of the quantity" [51]. When a mass spectrometer is used for quantitation, interference species can alter the ionization efficiency in the source when they co-elute with the target analyte, causing either ionization suppression or ionization enhancement [51]. These effects become particularly pronounced in notoriously complex matrices such as oils, flavorful extracts, and processed foods, which contain high levels of fats, proteins, pigments, and other co-extractives that can compromise analytical accuracy and method robustness.

The fundamental problem analysts face is that the matrix the analyte is detected in can either enhance or suppress the detector response to the presence of the analyte [50]. In mass spectrometry, particularly with electrospray ionization (ESI), analytes compete with matrix components for available charge during the desolvation process, leading to enhanced or suppressed ionization of the analyte depending on other constituents present in electrospray droplets [50]. The extent of matrix effect is widely variable and unpredictable—the same analyte can show different MS responses in different matrices, and the same matrix can affect different target analytes differently [51]. For a robust HPLC method within food applications, systematic strategies to evaluate, minimize, and compensate for these effects are therefore indispensable.

Evaluating and Quantifying Matrix Effects

Before implementing mitigation strategies, researchers must first assess the presence and magnitude of matrix effects. Several established methodologies provide complementary approaches for this evaluation, ranging from qualitative screening to quantitative measurement.

Table 1: Methods for Evaluating Matrix Effects

Method Name	Description	Output Type	Key Limitations
Post-Column Infusion [51]	Continuous infusion of analyte into HPLC effluent post-column while injecting blank matrix extract	Qualitative identification of suppression/enhancement zones	Does not provide quantitative data; laborious for multi-analyte methods
Post-Extraction Spike [51]	Comparison of analyte response in neat solution versus blank matrix spiked post-extraction	Quantitative measurement (e.g., % suppression/enhancement)	Requires blank matrix; single concentration level assessment
Slope Ratio Analysis [51]	Comparison of calibration curve slopes in neat solution versus matrix	Semi-quantitative across concentration range	Requires blank matrix; does not identify specific problematic regions

The post-column infusion method, pioneered by Bonfiglio et al., provides a qualitative assessment of matrix effects that helps identify retention time zones most likely to experience phenomena of ion enhancement or suppression [51]. The analysis is performed by injecting a blank sample extract through the LC-MS system while a post-column infusion of the analyte standard is introduced through a T-piece [51]. If a blank matrix is unavailable, the post-column infusion can be performed using a labeled internal standard instead of the analyte standard [51]. This approach is particularly valuable during method development as it visually reveals regions of significant ionization interference that might compromise quantitative accuracy.

For quantitative assessment, the post-extraction spike method developed by Matuszewski et al. compares the response of the analyte in a standard solution to that of the analyte spiked into a blank matrix sample at the same concentration [51]. Deviations from the responses of the two solutions are identified as ion enhancement or suppression. A modification of this approach, called "slope ratio analysis," expands this evaluation across the entire calibrated range instead of a single concentration level, providing a more comprehensive understanding of matrix effects [51].

Figure 1: Decision workflow for selecting appropriate matrix effect evaluation methods based on method development stage and blank matrix availability

Sample Preparation Strategies for Matrix Effect Minimization

Effective sample preparation represents the first line of defense against matrix effects in complex food samples. The primary goal is to selectively extract target analytes while removing potential interferents, thereby reducing the complexity of the final extract introduced into the HPLC system.

Selective Extraction Techniques

For complex food matrices, traditional liquid-liquid extraction (LLE) often proves insufficiently selective. Modern approaches include Supported Liquid Extraction (SLE), which offers the same partitioning principles as LLE but eliminates emulsion formation, facilitates high-throughput workflows, allows for automation, and provides more reliable and consistent results [52]. SLE is particularly valuable for aqueous food samples such as beverages, dairy products, and plant extracts. The technique involves a two-step protocol where samples are introduced in an aqueous or polar organic solution, followed by elution with a nonpolar organic, immiscible solvent [52]. Method development for SLE is notably efficient, as multiple solvents and different concentrations can be screened for extraction efficiency in less than 15 minutes, with subsequent analysis for matrix effects, ion suppression, and overall recovery and reproducibility [52].

Solid-Phase Extraction (SPE) provides even greater selectivity and is highly customizable for specific analyte classes. The selection of appropriate SPE sorbents depends on the characteristics of the sample matrix and the target analytes [52]. For food analysis, common sorbent chemistries include:

• C18: For non-polar to moderately polar compounds
• Silica: For polar compounds
• Ion-Exchange: For charged analytes
• Primary/Secondary Amine (PSA): Effective for removing organic acids, fatty acids, sugars, and pigments
• Graphitized Carbon Black (GCB): Excellent for pigment removal [52]

The stepwise SPE process involves conditioning the cartridge with an appropriate solvent, loading the sample, washing with solvents that remove interferences without eluting analytes, and finally eluting the purified analytes with a suitable solvent [53]. When developing SPE methods, researchers should analyze fractions to determine where analytes are lost—if analytes emerge during sample loading, this indicates incomplete bonding to the stationary phase; if present in wash solvents, the wash solvent is too strong; and if absent from the eluate, a stronger elution solvent is required [52].

Integrated Extraction-Cleanup Approaches

The QuEChERS (Quick, Easy, Cheap, Effective, Rugged, Safe) method has revolutionized multiresidue analysis in food matrices, particularly for pesticides and contaminants. The original QuEChERS approach involves extraction with acetonitrile followed by a dispersive SPE clean-up step [52]. For pigmented samples, the AOAC 2007.01 method provides an excellent starting point, using a combination of magnesium sulphate (MgSO₄) salts to induce phase separation between water content in the sample and an acetonitrile layer, while sodium acetate (NaOAc) acts as a buffer to control pH [52]. The dSPE portion typically includes MgSO₄ to remove excess water, PSA to remove organic acids, fatty acids, sugars, and anthocyanin pigments, while GCB removes additional pigments from the sample [52].

Table 2: Comparison of Sample Preparation Techniques for Complex Food Matrices

Technique	Mechanism	Best Suited Matrices	Advantages	Limitations
Supported Liquid Extraction (SLE) [52]	Partitioning between aqueous sample and immiscible organic solvent	Aqueous foods (beverages, dairy, plant extracts)	Minimal emulsions; easy method development; automatable	Limited for non-polar or solid matrices
Solid-Phase Extraction (SPE) [53] [52]	Selective adsorption based on functionalized sorbents	Oils, processed foods, multiclass analytes	High selectivity; customizable; effective cleanup	Method development can be complex; cartridge variability
QuEChERS [52]	Solvent extraction followed by dispersive SPE cleanup	Fruits, vegetables, grains, spices	Rapid; cost-effective; high throughput	May require matrix-specific modifications
Protein Precipitation [53]	Denaturation and sedimentation of proteins	Protein-rich foods (dairy, meat, legumes)	Simple; rapid; effective for protein removal	Limited removal of other interferents

Chromatographic and Instrumental Solutions

When sample preparation alone proves insufficient to eliminate matrix effects, chromatographic and instrumental adjustments provide additional avenues for mitigation.

Chromatographic Separation Optimization

Enhanced chromatographic separation represents one of the most effective approaches for reducing matrix effects by temporally separating analytes from co-eluting interferents. The fundamental strategy involves modifying the chromatographic conditions to shift the retention times of either the analytes or the matrix components, thereby minimizing their co-elution. This can be achieved through several parameters: adjusting the mobile phase composition (including pH modifiers that alter ionization states), employing gradient elution rather than isocratic methods, utilizing columns with different stationary phases (C18, phenyl, pentafluorophenyl), or selecting columns with smaller particle sizes (sub-2μm) that provide higher efficiency [51] [54].

The role of column chemistry is particularly important when dealing with complex food matrices. For instance, the use of specialized columns such as the Inertsil ODS-3 C18 column (250 mm, 4.6 mm, 5 μm, and 100 Å) has demonstrated effectiveness in separating challenging analytes like favipiravir from matrix components [16]. When developing methods for complex food samples, employing a column with different selectivity or a longer column can provide the necessary resolution to separate analytes from matrix components that cause ionization suppression or enhancement in mass spectrometry [51].

Mass Spectrometric and Detection Parameter Adjustments

For mass spectrometric detection, several instrumental approaches can minimize matrix effects. A simple and common practice is using a divert valve to switch the flow coming from the column to waste during periods when matrix interferences are eluting, resulting in less ion source contamination [51]. Additionally, switching ionization sources from electrospray ionization (ESI) to atmospheric pressure chemical ionization (APCI) may reduce matrix effects, as APCI is generally less prone to ionization suppression because the mechanism occurs primarily in the gas phase rather than the liquid phase [51].

Optimizing source parameters is another valuable strategy. Increasing the declustering potential or source temperature can promote fragmentation of matrix components while preserving analyte signal. Similarly, adjusting nebulizer and drying gas flows can improve desolvation efficiency, though these parameters require systematic optimization as their effects are often analyte-dependent [51]. For targeted quantification, selecting multiple reaction monitoring (MRM) transitions with higher specificity can help avoid contributions from isobaric matrix components, though this approach requires validation to ensure the selected transitions are not affected by matrix-derived ions.

Analytical Quality by Design for Robust Methods

The Analytical Quality by Design (AQbD) framework provides a systematic methodology for developing robust HPLC methods that are inherently resilient to matrix effects. AQbD emphasizes proactive method understanding and control based on sound science and quality risk management [54]. This approach integrates method development, validation, and continuous improvement within a holistic framework, making it particularly valuable for complex food matrices where variability is inevitable.

The AQbD process begins with defining the Analytical Target Profile (ATP), which outlines the method's purpose and predefined performance criteria, including accuracy, precision, linearity, robustness, sensitivity, and increasingly, eco-friendliness [54]. Subsequently, Critical Quality Attributes (CQAs) such as resolution, retention time, and peak symmetry are identified and linked to Critical Method Parameters (CMPs) including flow rate, mobile phase composition, column temperature, and detection wavelength [54]. Risk assessment tools such as Ishikawa diagrams and Failure Mode and Effects Analysis (FMEA) then help prioritize variables that significantly affect method quality [54].

Design of Experiments (DoE) serves as a central AQbD tool, enabling the systematic evaluation of multiple factors and their interactions through techniques such as factorial design, Box-Behnken, or central composite design [54]. This approach efficiently identifies the optimal design space for CMPs while reducing experimental trials. The outcome is the establishment of a Method Operable Design Region (MODR), representing the multidimensional region within which the method delivers acceptable performance, allowing minor adjustments without revalidation [54]. A notable example of AQbD implementation demonstrated the development of a robust reversed-phase HPLC method for favipiravir quantification using a d-optimal experimental design to study the impact of solvent ratio, buffer pH, and column type on critical chromatographic parameters [16].

Figure 2: Analytical Quality by Design workflow for developing robust HPLC methods resilient to matrix effects in complex food samples

Internal Standardization and Calibration Approaches

When matrix effects cannot be sufficiently eliminated through sample preparation or chromatographic separation, compensation through appropriate calibration strategies becomes essential. The internal standard method represents one of the most potent approaches for mitigating matrix effects on quantitation [50].

Internal Standard Selection and Application

The internal standard method involves adding a known amount of a reference compound to every sample before analysis [50]. For optimal correction of matrix effects, the internal standard should closely mimic the behavior of the target analyte throughout sample preparation, chromatography, and detection. Stable isotope-labeled internal standards (SIL-IS) represent the gold standard because they exhibit nearly identical chemical properties to the analytes while being distinguishable by mass spectrometry [51]. For example, if the target analyte is toluene, ¹³C-labeled toluene serves as an ideal internal standard since it behaves similarly to toluene yet can be detected separately via MS due to the mass shift [50].

The quantification process then uses ratios rather than absolute responses. The y-axis represents the ratio of the signal for the target analyte to the signal for the internal standard, while the x-axis represents the ratio of the target analyte concentration to the internal standard concentration [50]. This approach effectively corrects for both preparation inconsistencies and ionization variability in the mass spectrometer. When isotope-labeled standards are unavailable, structural analogs with similar physicochemical properties may be used, though they generally provide less accurate compensation [51].

Alternative Calibration Strategies

When appropriate internal standards are unavailable or impractical, alternative calibration approaches can mitigate matrix effects. Matrix-matched calibration involves preparing calibration standards in blank matrix that closely resembles the sample composition, thereby experiencing similar matrix effects [51]. The standard addition method represents another robust approach, particularly useful when a blank matrix is unavailable. This technique involves spiking samples with known concentrations of analyte and extrapolating to determine the original concentration [51]. However, standard addition increases analytical time and may not be practical for high-throughput applications.

For multi-analyte methods where matrix effects vary between compounds, the surrogate matrix approach may be employed, using a alternative matrix that demonstrates similar analytical behavior to the original matrix [51]. This strategy requires demonstration of similar MS response for the analyte in both original and surrogate matrix to ensure validity [51].

Table 3: Calibration Strategies for Compensating Matrix Effects

Calibration Method	Principle	When to Use	Advantages	Limitations
Internal Standardization [50]	Addition of reference compound to all samples	When suitable internal standards available	Corrects for preparation and ionization variability; high precision	Isotope-labeled standards can be expensive
Matrix-Matched Calibration [51]	Calibration in blank matrix identical to samples	When blank matrix is available	Compensates for matrix effects directly	Requires substantial blank matrix; may not match all samples
Standard Addition [51]	Spiking samples with known analyte increments	When blank matrix unavailable; few samples	Accounts for specific sample matrix; high accuracy	Labor-intensive; not practical for large batches
Surrogate Matrix [51]	Calibration in alternative matrix with similar behavior	When original matrix is scarce or variable	Enables calibration when original matrix limited	Must demonstrate equivalence to original matrix

Application-Specific Protocols for Challenging Food Matrices

Protocol for Oily and Fatty Food Matrices

Oily matrices present particular challenges due to their high lipid content, which can cause significant ionization suppression and column fouling. A robust protocol begins with SLE if the oil can be mixed with a strong organic solvent, dried down, and reconstituted with deionized water [52]. For SPE-based approaches, silica-based cartridges effectively retain non-polar interferents while allowing elution of more polar analytes. An optimized protocol includes:

• Conditioning: 5 mL each of hexane and dichloromethane
• Sample Loading: Oil dissolved in minimal hexane
• Wash: 5 mL hexane:ethyl acetate (90:10) to remove triglycerides
• Elution: 5 mL ethyl acetate:methanol (80:20) to recover analytes
• Concentration: Nitrogen evaporation at 40°C and reconstitution in mobile phase [52]

For comprehensive cleanup, enhanced filtration techniques utilizing membrane filters specifically designed for lipid removal can be incorporated post-extraction [53].

Protocol for Pigmented and Polyphenol-Rich Extracts

Highly pigmented food samples such as spices, herbs, and certain vegetables require specialized cleanup to remove chlorophyll, anthocyanins, and other natural pigments that cause matrix effects. A modified QuEChERS approach proves particularly effective:

• Extraction: 10 g sample homogenized with 10 mL acetonitrile and ceramic homogenizer
• Partitioning: Addition of 4 g MgSO₄, 1 g NaCl, 1 g trisodium citrate dihydrate, 0.5 g disodium hydrogen citrate sesquihydrate
• dSPE Cleanup: 150 mg MgSO₄, 25 mg PSA, 25 mg C18, and 7.5 mg GCB per mL supernatant [52]
• Vortex and Centrifuge: 1 minute vortexing followed by 5 minutes centrifugation at 4000 rpm

The GCB specifically targets pigment removal, though analysts should note that it may also retain planar pesticides or other planar analytes, requiring method verification for specific targets [52].

Protocol for Protein-Rich Processed Foods

Processed foods often contain complex mixtures of proteins, carbohydrates, and fats that necessitate comprehensive sample preparation. Protein precipitation provides an essential first step:

• Precipitation: Add precipitant (typically acetonitrile or methanol) to sample in a 2:1 or 3:1 ratio [53]
• Mixing and Centrifugation: Vortex for 30 seconds, centrifuge at 10,000 × g for 10 minutes
• Supernatant Collection: Transfer supernatant for additional cleanup if necessary

For particularly challenging matrices, this can be followed by SPE using mixed-mode cartridges that combine reversed-phase and ion-exchange mechanisms, providing superior selectivity for removing interferences while retaining analytes of interest [53] [52].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 4: Essential Research Reagents for Mitigating Matrix Effects in Food Analysis

Reagent Category	Specific Examples	Primary Function	Application Notes
SPE Sorbents [53] [52]	C18, C8, Phenyl, CN-propyl, Silica, PSA, GCB, Ion-exchange resins	Selective retention of analytes or interferents	C18 for non-polar compounds; PSA for sugars/acids; GCB for pigments
Extraction Salts [52]	MgSO₄, NaCl, NaOAc, Trisodium citrate	Phase separation in QuEChERS; dehydration	MgSO₄ generates heat during hydration; buffer salts control pH
Precipitation Reagents [53]	Acetonitrile, Methanol, Trichloroacetic acid	Protein denaturation and removal	Acetonitrile generally provides cleaner extracts than methanol
Internal Standards [51] [50]	Stable isotope-labeled analogs, Structural analogs	Compensation for matrix effects and preparation losses	Isotope-labeled preferred for LC-MS; analogs for UV detection
Mobile Phase Additives [16] [54]	Ammonium formate/acetate, Phosphoric acid, Formic acid	pH control; ion pair formation; volatility	Volatile additives essential for MS detection; buffers for stability
Green Solvent Alternatives [55] [54] [56]	Ethanol, Water, Supercritical CO₂, Deep Eutectic Solvents	Reduced environmental impact; safer handling	Ethanol effectively replaces acetonitrile in many applications

Matrix effects in complex food samples present significant challenges that require a systematic, multifaceted mitigation strategy. Successful approaches combine appropriate sample preparation techniques such as SLE, SPE, or QuEChERS with chromatographic optimization and intelligent calibration methods. The implementation of an AQbD framework ensures method robustness by systematically identifying and controlling critical parameters that influence matrix effects. For ongoing method verification, regular assessment using post-column infusion or post-extraction spiking provides early detection of matrix effect changes due to sample matrix variability. By integrating these complementary strategies, analysts can develop reliable, robust HPLC methods capable of producing accurate quantification even in the most challenging food matrices, thereby supporting the demanding requirements of modern food analysis, quality control, and regulatory compliance.

For researchers in food and pharmaceutical analysis, the robustness of an High-Performance Liquid Chromatography (HPLC) method is paramount. Robustness is defined as a measure of the method's capacity to remain unaffected by small, deliberate variations in method parameters, indicating its reliability during normal usage [57]. In the context of a broader thesis on robustness testing, this application note addresses a critical triad of factors often overlooked during method development: temperature, humidity, and instrument variance.

Environmental factors and instrumental differences can significantly impact chromatographic results, leading to inaccurate quantification, compromised food safety assessments, and unreliable stability studies. Method robustness testing against these variables is not merely a regulatory formality but a fundamental practice to ensure data integrity and method transferability across laboratories and instrument platforms. This document provides detailed protocols and best practices to systematically evaluate and control these factors, ensuring your HPLC methods yield consistent, reliable, and reproducible data.

The Impact of Environmental and Instrumental Variability

Temperature and Humidity Effects

Temperature and humidity are critical environmental variables that can directly affect the chemical stability of analytes and the performance of analytical reagents.

• Analyte Degradation: Many bioactive compounds, such as nutraceuticals, are susceptible to thermo-oxidative degradation. For instance, a stability study on resveratrol in nutraceutical tablets demonstrated that degradation kinetics are highly temperature-dependent. However, this relationship can be complex and may not follow simple Arrhenius behavior, meaning accelerated stability tests at high temperatures can sometimes overestimate degradation at normal storage conditions (e.g., 25°C) [58].
• Pigment and Additive Stability: The stability of natural pigments like betalains is profoundly affected by storage conditions. Research shows that relative humidity (RH) is a dominant factor; betalains encapsulated in cryogels showed significantly higher retention (72-82% for betanin/isobetanin) at 32% RH and 4°C compared to storage at 83% RH, where a major decrease occurred. High humidity can promote hydrolysis, leading to the degradation of sensitive compounds [59].
• Chemical Reaction Rates: As fundamental chemical principles dictate, elevated temperature and humidity can accelerate hydrolysis, oxidation, and isomerization reactions, compromising the accuracy of quantitative analysis in food and supplement matrices [59] [58].

Instrument and Platform Variance

The quantification of analytes can be significantly influenced by the choice of analytical instrument, even when using the same validated method.

• qPCR Instrument Variance: A systematic study on quantitative PCR (qPCR) instruments, highly relevant for GMO testing in food, provides a powerful analogy for HPLC. The study found that while validated methods generally perform within acceptance criteria across different platforms, statistical analysis of variance (ANOVA) revealed that the quantification results were significantly affected by the instrument model. The study concluded that interactions between platforms and analyte levels exist, and some instruments deviated significantly from others [57]. This underscores that instrument variance is a non-negligible source of uncertainty.
• HPLC Solvent Delivery and Detection: Differences in HPLC instrument modules, such as pump pressure stability, gradient mixing accuracy, and detector sensitivity, can lead to variations in retention times, peak area, and resolution, thereby impacting the robustness of the quantitative results.

Table 1: Impact of Environmental Factors on Analyte Stability

Environmental Factor	Impact on Analysis	Example from Literature
Elevated Temperature	Accelerates degradation kinetics; can lead to overestimation of degradation rates in accelerated studies [58].	Resveratrol degradation in tablets showed non-Arrhenius behavior, complicating shelf-life prediction [58].
High Relative Humidity	Promotes hydrolysis of water-sensitive compounds; reduces stability of encapsulated bioactives [59].	Betalain retention was 72-82% at 32% RH/4°C vs. significant loss at 83% RH [59].
Instrument Variance	Introduces significant bias in quantification; instrument-analyte level interactions exist [57].	qPCR GMO quantification varied significantly across six different instrument platforms [57].

Experimental Protocols for Robustness Testing

Protocol 1: Testing Robustness Against Temperature and Humidity

This protocol is designed to evaluate the stability of an analyte in its sample matrix under different storage conditions, which is critical for defining sample handling procedures and predicting shelf-life.

1. Objective: To determine the effect of temperature and relative humidity on the stability of a target analyte in a finished product (e.g., tablet, powder).

2. Materials and Reagents:

• Analyte reference standard.
• Finished product samples (e.g., nutraceutical tablets).
• HPLC-grade solvents for extraction and mobile phase.
• Hermetic stability chambers or desiccators with saturated salt solutions.
• HPLC system with appropriate detector (e.g., DAD).

3. Procedure:

• Step 1: Sample Preparation. For each storage condition, prepare multiple sample aliquots. For solid formulations like tablets, grind a representative number of tablets into a homogeneous powder [58].
• Step 2: Controlled Storage. Store sample aliquots in stability chambers set at predefined conditions. A standard set includes:
  • Long-term storage: 25°C / 60% RH
  • Intermediate storage: 30°C / 65% RH
  • Accelerated storage: 40°C / 75% RH [58]
  • Note: Control humidity using saturated salt solutions (e.g., MgCl₂ for 32% RH, KCl for 83% RH) in sealed containers [59].
• Step 3: Time-Point Analysis. Remove samples at predetermined time intervals (e.g., t=0, 3, 6, 12, 24 months). For each time point, perform extraction in triplicate.
  • Extraction Example: Weigh 0.05 g of powder, dilute with 1 mL of a 75:25 (v/v) water-methanol mixture, and sonicate [58]. Centrifuge and filter the supernatant before HPLC analysis.
• Step 4: Chromatographic Analysis. Analyze all extracts using the validated HPLC method. Quantify the analyte based on a calibrated reference standard.
• Step 5: Data Analysis. Plot the residual analyte concentration (%) versus time for each storage condition. Calculate degradation rate constants (k) at each temperature and model the temperature dependence to predict shelf-life, being cautious of potential non-Arrhenius behavior [58].

Protocol 2: Testing Robustness Across HPLC Instruments

This protocol assesses the transferability of an HPLC method between different instruments within the same laboratory or across collaborating labs.

1. Objective: To verify that a validated HPLC method provides comparable results when deployed on different HPLC instrument systems.

2. Materials and Reagents:

• Identical batches of mobile phase solvents, standards, and sample aliquots.
• Multiple HPLC systems (from the same or different manufacturers), ideally with different configurations (e.g., pump series, detector models).
• Same specification of HPLC column (e.g., C18, 150 mm x 4.6 mm, 5 µm).

3. Procedure:

• Step 1: Standardized Materials. Prepare a single, large batch of mobile phase, standard solution, and sample homogenate. Aliquot and freeze samples at -20°C to minimize degradation, ensuring all instruments test identical material [57].
• Step 2: System Suitability. On each HPLC instrument, perform system suitability tests using the standard solution to ensure each system meets predefined criteria (e.g., plate count, tailing factor, RSD of replicate injections) before proceeding.
• Step 3: Cross-Instrument Analysis. On each instrument, analyze the calibration standards and test samples in triplicate, following the identical method (flow rate, gradient, column temperature, injection volume).
• Step 4: Data Collection and Comparison. For each instrument, record key performance parameters: retention time, peak area, resolution of critical peak pairs, and resulting quantification values for the test samples.
• Step 5: Statistical Evaluation. Perform statistical analysis (e.g., one-way and two-way ANOVA) to determine if there are significant differences in quantification results between the instruments. As demonstrated in the qPCR study, significant interactions between instruments and analyte concentrations may be present and should be investigated [57].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Key Reagents and Materials for Environmental Robustness Studies

Item	Function / Application	Example Usage & Rationale
Saturated Salt Solutions	To precisely control relative humidity in sealed stability chambers [59].	MgCl₂ for ~32% RH, KCl for ~83% RH in betalain stability studies [59].
Certified Reference Standards	For accurate quantification and calibration during stability-indicating methods.	Using resveratrol standard (purity >98%) to quantify degradation in tablets [58].
HPLC Columns with Alternative Selectivity	To improve separation efficiency and achieve method goals with greener solvents [60].	Using a C18-perfluorophenyl (PFP) phase instead of C18 for better selectivity, allowing for shorter run times and lower solvent consumption [60].
Green Solvents (e.g., Methanol, Ethanol)	More sustainable alternatives to toxic solvents like acetonitrile in reversed-phase HPLC [60].	Method re-evaluation may allow substitution of acetonitrile with methanol or ethanol for less demanding applications, reducing environmental impact [56] [60].
In Silico Method Modeling Software	To optimize method conditions virtually, reducing solvent waste and lab time during method development and transfer [60].	Predicting the outcome of solvent substitution (e.g., acetonitrile to methanol) and column chemistry changes before laboratory experimentation [60].

Workflow and Relationship Diagrams

Robustness testing against environmental and instrumental variables is a critical pillar of method validation for HPLC applications in food and pharmaceutical research. By systematically implementing the protocols outlined in this application note, scientists can proactively identify and control the influence of temperature, humidity, and instrument differences. This rigorous approach ensures that analytical methods are not only scientifically valid but also reliable and transferable, thereby upholding the highest standards of data quality, supporting regulatory compliance, and ultimately ensuring product safety and efficacy.

The demand for robust High-Performance Liquid Chromatography (HPLC) methods in food analysis is paramount, driven by the need to ensure food safety, authenticity, and compliance with global regulatory standards. Robustness—defined as a method's capacity to remain unaffected by small, deliberate variations in procedural parameters—is a critical indicator of its reliability during normal use [3]. Traditional, manual approach to method development and robustness testing are often time-consuming, resource-intensive, and susceptible to human bias. Within the context of a thesis on food applications, this application note details modern, efficient protocols leveraging Automation and Artificial Intelligence (AI) to streamline the creation and evaluation of robust HPLC methods, aligning with the principles of Green Analytical Chemistry (GAC) to minimize environmental impact [13].

The Modern Toolbox: AI and Automated Systems

The integration of AI and automation is transforming HPLC method development from an art into a data-driven science.

Artificial Intelligence and Machine Learning

AI and Machine Learning (ML) offer powerful capabilities for modeling separation spaces and predicting optimal method parameters. Software tools can predict physicochemical properties of analytes (e.g., pKa, logD) to suggest better starting conditions, thereby reducing initial scouting work [61]. However, current research indicates that AI's role is often augmentative rather than fully autonomous. A recent study comparing AI-predicted and in-lab optimized methods for separating a drug mixture found that AI-generated conditions required human refinement to achieve practical application; the initial AI method resulted in longer analysis times and higher solvent consumption, whereas the scientist-optimized method was superior in both analytical efficiency and greenness [62]. Key considerations include:

• Data Quality: ML models are only as good as the data they are trained on. A lack of high-quality, ground-truth data remains a significant challenge for developing robust general-purpose algorithms [63].
• Explorative Potential: Reinforcement learning and other ML techniques show significant promise for explorative method development, with research groups actively investigating their applicability to optimize separations [63].
• Simulation Software: Virtual modeling tools like ACD/LC Simulator and DryLab allow for the creation of in-silico models of a separation. These models enable the prediction of method performance and robustness across a wide range of conditions without the need for extensive laboratory work, facilitating the identification of a robust "Method Operable Design Region (MODR)" [10].

Automated Laboratory Hardware

Automation hardware is critical for efficiently executing the experiments designed by AI and software. Key technologies include:

• Automated Solvent Switching: Enables the scouting of up to different mobile phases per channel without manual bottle exchange and system purging [64].
• Automated Column Switching: Allows for the sequential testing of multiple stationary phases, eliminating the need for manual column swapping and ensuring consistent fluidic connections [64].
• Automated Sample Preparation: Systems that automate dilution, calibration curve creation, and other pre-analysis steps reduce operator burden and variability, enhancing overall method ruggedness [65].

Quantitative Comparison of Modern Method Development Approaches

The following table summarizes the performance characteristics of different modern development strategies, highlighting their impact on robustness and efficiency.

Table 1: Performance Comparison of HPLC Method Development Approaches

Development Approach	Key Features	Impact on Robustness	Reported Efficiency Gains
Analytical Quality by Design (AQbD)	- Systematic, risk-based approach- Defines a Method Operable Design Region (MODR)- Uses experimental design (e.g., D-optimal) and Monte Carlo simulations [16]	- High; robustness is built into the method by design [16]	- Reduces method adoption time and expensive re-testing [61]
AI-Predicted Methods	- AI suggests initial conditions based on compound properties- May use reinforcement learning [63]	- Variable; often requires expert refinement to achieve robustness [62]	- Can rapidly generate a starting point, but may not be optimal [62]
In-Lab Optimization with Software	- Uses simulation software (e.g., DryLab, ACD) to model separations from a minimal set of experiments [10]	- High; software can map the robustness space to identify optimal, stable conditions [10]	- Reduces experimental runs from months to days [64]
Fully Automated Scouting	- Integrated software and hardware for hands-off screening of columns and solvents [64]	- Enhanced consistency and reproducibility by removing manual intervention	- Development time reduced from weeks to days [64]

Experimental Protocols

Protocol 1: Robustness Evaluation Using a Screening Design

This protocol provides a systematic, multivariate approach to assess a method's robustness, as recommended by ICH and USP guidelines [3].

1. Define Objective and Select Factors:

• Objective: To identify critical methodological parameters and establish system suitability limits.
• Factor Selection: Choose factors specified in the method document. For a reversed-phase HPLC method for food analysis (e.g., analyzing bioactive compounds or contaminants), typical factors include:
  • pH of the mobile phase buffer: A variation of ±0.1 units [3] [10].
  • Flow rate: A variation of ±0.1 mL/min [3].
  • Column temperature: A variation of ±2°C [3].
  • Volume fraction of organic solvent (%B): A variation of ±1% absolute [10]. This is critical as a 1% change can alter retention times significantly.

2. Define Responses and Acceptable Limits:

• Determine the critical responses to monitor. These typically include:
  • Resolution (Rs) of the critical peak pair (must be >1.5).
  • Tailing factor (Tf) for main analytes (must be within specified range, e.g., 0.8-1.5).
  • Retention time (tR) of the first and last peaks.
  • Theoretical plate count (N).

3. Select an Experimental Design:

• A Plackett-Burman design is highly efficient for screening a larger number of factors (e.g., 5-7) where only the main effects are of interest [3]. This design tests factors at two levels (high, +1 and low, -1) in a minimal number of runs (e.g., 12 runs for up to 11 factors).

4. Execute the Experiment and Analyze Data:

• Perform the experimental runs in a randomized order to avoid bias.
• Inject system suitability standards or test solutions for each run.
• Analyze the data using statistical software. Effects plots (Pareto or Half-Normal) are used to identify which factors have a statistically significant effect on the responses.

5. Establish System Suitability Criteria:

• Based on the results, set appropriate system suitability test limits to ensure the method remains robust during routine use, even with expected instrumental and preparative variations.

Protocol 2: Implementing an AQbD Approach for Green Method Development

This protocol outlines the use of AQbD to develop a robust and environmentally friendly HPLC method, as demonstrated for pharmaceuticals and applicable to food analysis [16].

1. Define Analytical Target Profile (ATP):

• The ATP states the method's purpose and required performance criteria (e.g., "The method must quantify FAV in fortified food samples with an accuracy of 95-105% and a precision of RSD <2%").

2. Risk Assessment:

• Use a tool like an Ishikawa (fishbone) diagram to identify all potential factors that could impact the ATP.
• High-risk factors (e.g., buffer pH, column temperature, gradient time) are selected for further study.

3. Experimental Design and MODR Definition:

• A D-optimal design can be used to efficiently study the impact of the selected high-risk factors (X1: ratio of solvent, X2: pH of the buffer, X3: column type) on critical responses (Y1: retention time, Y2: tailing factor, Y3: theoretical plates) [16].
• Conduct the experiments and use modeling software (e.g., MODDE Pro) to build mathematical models linking factors to responses.
• The Method Operable Design Region (MODR) is established as the multidimensional space where the method meets the ATP criteria. A Monte Carlo simulation can then be used to verify the probability of achieving success within the MODR and to select a robust set point [16].

4. Method Validation and Control:

• Validate the method at the robust set point as per ICH/USP guidelines.
• The control strategy includes the defined system suitability tests derived from the MODR to ensure ongoing method performance.

Workflow Visualization

The following diagram illustrates the integrated AQbD and robustness testing workflow for developing a sustainable HPLC method.

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 2: Key Reagents and Materials for Robust HPLC Method Development

Item	Function / Role in Robustness	Example / Consideration
Buffer Salts	Controls mobile phase pH, critical for ionizable analytes. Small pH variations can significantly impact retention and selectivity.	Disodium hydrogen phosphate [16]; Phosphate, acetate. Use high-purity salts and control pH precisely (±0.1 unit).
Organic Solvents	Modifies retention and selectivity in reversed-phase chromatography. Proportion is a high-risk factor for robustness.	Acetonitrile, Methanol [10]. Measure volumes accurately; a ±1% absolute variation is a typical robustness test level [10].
Chromatographic Columns	The stationary phase is a primary driver of separation. Different lots and brands can vary.	C18, Phenyl-Hexyl [62]. Include column-to-column variability (e.g., from different lots) in robustness/ruggedness testing [3].
pH Standard Buffers	For accurate calibration of the pH meter during mobile phase preparation.	Certificated reference materials traceable to national standards are essential for reproducible pH adjustment.
Chemical Modifiers	Improves peak shape and separation for certain analytes.	Trifluoroacetic Acid (TFA) [62]. Concentration should be included as a factor in robustness studies if used.
Greenness Assessment Tools	Software/metrics to evaluate the environmental impact of the developed method.	AGREE, BAGI, Analytical Eco-Scale [62] [13]. Used to score and improve method sustainability.
Method Scouting & Optimization Software	AI/ML-driven software for predicting optimal starting conditions and modeling the separation space.	ChromSwordAuto, ACD/Method Selection Suite, Fusion QbD [61] [64]. Reduces development time and consumption of materials.

Implementing a Continuous Monitoring System for Long-Term Method Performance

In the context of High-Performance Liquid Chromatography (HPLC) method robustness testing for food applications, ensuring consistent analytical performance over time is not merely a regulatory expectation but a scientific necessity. A continuous monitoring system provides the framework for detecting subtle performance degradation, enabling proactive maintenance and ensuring data integrity throughout a method's lifecycle. For researchers and scientists in pharmaceutical development and food safety, implementing such a system transforms method performance from a point-in-time validation exercise into a dynamic, data-driven process [11] [66].

This document outlines application notes and detailed protocols for establishing a continuous monitoring system, specifically framed within robustness testing for HPLC methods in food analysis. The approach integrates statistical process control, systematic documentation, and defined decision-making protocols to maintain method robustness for detecting contaminants, adulterants, and nutrients in complex food matrices [67].

Core Principles of HPLC Performance Monitoring

Continuous monitoring tracks critical chromatographic parameters over time to establish a performance baseline and identify significant deviations. This is foundational for methods used in food safety applications, such as detecting veterinary drug residues, chemical contaminants, or ensuring organic authenticity [67] [66]. The key parameters to monitor include:

• Peak Symmetry/Tailing Factor: Indicates column health and potential secondary interactions. A symmetry factor close to 1.0 is optimal, with values typically ranging between 0.8 and 1.5 for a healthy column. Increased tailing often signals column degradation or contamination [68] [66].
• Retention Time Stability: A critical indicator of method robustness. Acceptable variations are typically within ±2% for consistent analyte detection. Significant drift suggests changes in the stationary phase, mobile phase composition, or flow rate [66] [69].
• Theoretical Plate Count (Efficiency): Measures the column's separation efficiency under specified conditions. A gradual decline in plate numbers indicates column packing degradation [68] [66].
• Resolution (Rs): Critical for separating closely eluting peaks, especially in complex food matrices. A resolution of ≥2.0 between critical peak pairs provides a safety margin against method deterioration [68].
• System Pressure: Unexpected pressure increases may suggest column blockage, particle degradation, or mobile phase incompatibility [66].

Table 1: Key Performance Parameters and Their Acceptance Criteria

Parameter	Target Value	Acceptance Limit	Significance in Monitoring
Retention Factor (k)	k > 2 for first peak [68]	k > 1 [68]	Ensures peaks are sufficiently retained away from the solvent front.
Resolution (Rs)	Rs ≥ 2.0 [68]	Rs ≥ 1.5 [68]	Ensures baseline separation between critical peak pairs.
Tailing Factor (Tf)	0.9 - 1.2 [68]	≤ 1.7 [68]	Indicates column health and potential secondary interactions.
Theoretical Plates (N)	Per column specification [66]	≥ 80% of initial value	Measures column separation efficiency.
Retention Time Drift	Stable	≤ ±2% [66]	Indicates consistency of the chromatographic system.

Experimental Protocol: Establishing a Continuous Monitoring System

Materials and Reagents

Table 2: Essential Research Reagent Solutions and Materials

Item	Function/Application	Specification Notes
HPLC System	Chromatographic separation and analysis.	UV-Vis or DAD detector; ability to control column temperature [70].
Reference Standard	System suitability testing and calibration.	Certified reference material for the target analyte in a food matrix [69].
Chromatography Column	Stationary phase for separation.	C18 column or as specified in the method [6].
Mobile Phase Solvents	Liquid phase for analyte transport.	HPLC-grade water, acetonitrile, methanol; buffers (e.g., ammonium acetate) [20] [70].
System Suitability Test Mix	Performance verification.	Contains analytes to measure key parameters (k, Rs, Tf, N) [68].
Data Acquisition System	Records and stores chromatographic data.	Chromatography Data System (CDS) or Electronic Lab Notebook (ELN) for trend analysis [66].

Workflow for Implementation

The following workflow outlines the key stages for implementing a continuous monitoring system, from initial setup to data-driven decision-making.

Step-by-Step Protocol

Phase 1: Establish Performance Baseline

• Column Characterization: Using a new or freshly regenerated column, perform five consecutive injections of the system suitability test mixture under the defined chromatographic conditions [66] [6].
• Data Collection: For each injection, record the retention time, peak area, peak asymmetry/tailing factor, theoretical plate count (N), and resolution (Rs) for all critical analytes. Also, record the system pressure [66].
• Calculate Baseline Metrics: Calculate the mean and standard deviation for each parameter across the five replicates. These values establish the initial performance baseline [66].
• Documentation: Document all baseline metrics, along with column details (serial number, lot), mobile phase composition, and instrument ID in a standardized tracking template or Electronic Laboratory Notebook (ELN) [66].

Phase 2: Routine Monitoring and Data Collection

• Frequency: Perform system suitability testing with a single injection at the beginning of each analytical sequence or according to a risk-based schedule (e.g., daily, or every 10 samples) [68] [66].
• Execution: Inject the system suitability test mix and record all parameters listed in Phase 1.
• Systematic Logging: Log all results in the tracking system alongside the sample run details, including sample matrix, number of injections, and any observed anomalies [66].

Phase 3: Data Analysis and Trend Identification

• Visualization: Use control charts to plot the recorded values for each key parameter (e.g., retention time, pressure, plate count) against the injection count or date [66].
• Trend Analysis: Analyze the charts for both random variation and significant trends, such as a steady increase in pressure or a gradual decrease in plate count [66].
• Statistical Process Control (SPC): Apply SPC rules to distinguish between common-cause (random) variation and special-cause (assignable) variation that requires intervention [66].

Table 3: Troubleshooting Common Performance Shifts

Observed Shift	Potential Causes	Corrective Actions
Gradual Pressure Increase	Column frit blockage; Particulate accumulation [66].	Filter samples and mobile phase; Flush column according to manufacturer's instructions.
Reduced Plate Count\n(Peak Broadening)	Column bed degradation; Contamination of stationary phase [68] [66].	Clean or regenerate the column; Replace column if cleaning is ineffective.
Increased Tailing Factor	Active sites on column; Contamination [68] [66].	Use a mobile phase with different pH or additives; Clean the column.
Retention Time Drift	Mobile phase composition change; Column temperature fluctuation; Stationary phase degradation [66].	Prepare mobile phase consistently; Check column oven temperature; Re-equilibrate column.

Data Analysis and Decision-Making

The core of continuous monitoring is transforming raw data into actionable intelligence. Statistical Process Control (SPC) methods, such as control charts, are used to objectively assess whether the analytical process remains in a state of statistical control [66]. The following decision pathway guides the response to monitoring data.

Protocol for Statistical Analysis:

• Construct Control Charts: For each key parameter (e.g., retention time of primary analyte), create an individual control chart (I-chart) and a moving range chart (MR-chart).
• Set Control Limits: Calculate the upper and lower control limits (UCL, LCL) for the I-chart as: ± 2.66 * MR, where MR is the average moving range [66].
• Apply Decision Rules: A process is considered out of control if:
  • One point falls outside the 3-sigma control limits.
  • Two of three consecutive points fall beyond the 2-sigma warning limits on the same side.
  • A run of seven (or more) consecutive points on one side of the centerline.
• Multivariate Analysis (Advanced): For complex methods, consider Principal Component Analysis (PCA) to monitor multiple correlated parameters simultaneously [66].

Application in Food Safety and Regulatory Compliance

In food applications, maintaining a robust HPLC method is critical for accurately monitoring contaminants like veterinary drugs, pesticides, and mycotoxins [67]. A continuous monitoring system directly supports regulatory compliance by providing documented, data-driven evidence that the analytical method remains fit-for-purpose over time and across different sample matrices [11] [69]. This is especially pertinent for methods validating organic certification through residue testing [67]. Furthermore, the principles of high-throughput green analytical technologies align with this proactive approach, as early detection of performance issues reduces solvent waste and re-analysis time [11].

Implementing a continuous monitoring system for HPLC methods is a fundamental practice for ensuring long-term reliability and robustness in food research and pharmaceutical development. By establishing a performance baseline, systematically tracking critical parameters, and applying statistical tools for trend analysis, laboratories can move from reactive troubleshooting to predictive, science-based method management. This structured approach not only safeguards data quality and product safety but also enhances operational efficiency and regulatory confidence.

Demonstrating Method Excellence: Validation Protocols and Comparative Analysis for Regulatory Compliance

Robustness testing represents a critical, yet sometimes underestimated, component of the analytical method validation framework for High-Performance Liquid Chromatography (HPLC) in food applications. The International Conference on Harmonization (ICH) defines robustness as "a measure of the capacity of an analytical procedure to remain unaffected by small, deliberate variations in method parameters and provides an indication of its reliability during normal usage" [7]. For researchers and scientists developing methods for complex food matrices, establishing robustness provides confidence that analytical methods will perform reliably when transferred between laboratories, instruments, and analysts, despite the inevitable minor variations that occur in practice.

This application note provides a detailed protocol for integrating robustness testing into HPLC method validation, with specific consideration for food applications where complex sample matrices and regulatory compliance present unique challenges.

Theoretical Foundations of Robustness Testing

Definitions and Significance

Robustness testing systematically evaluates the influence of various method parameters on analytical results. In pharmaceutical analysis, and by extension in food analysis for compounds like additives, contaminants, or bioactive molecules, robustness testing has evolved from a terminal validation check to an integral part of method optimization [7]. This paradigm shift recognizes that early identification of critical parameters enables method refinement before full validation, reducing transfer problems during inter-laboratory studies.

For food applications, robustness takes on additional importance due to several factors:

• Complex Matrices: Food samples contain diverse interfering compounds that may interact differently with chromatographic systems under slightly modified conditions.
• Regulatory Scrutiny: Food analytical methods must comply with stringent international standards and accreditation requirements.
• Sample Preparation Variability: Extraction efficiency and clean-up can vary with subtle changes in solvent composition, pH, or temperature.

Regulatory Context and Guidelines

While ICH guidelines provide the foundation for pharmaceutical method validation, food分析方法 typically follow Codex Alimentarius, AOAC International, and ISO standards that similarly emphasize method reliability. The core principles remain consistent: robustness testing should identify factors that may cause significant variability in assay responses and establish system suitability test (SST) limits to ensure method reliability [7].

Experimental Design for Robustness Testing

Factor Selection and Level Determination

The first step in robustness testing involves selecting factors potentially affecting method performance and determining appropriate levels for testing.

Table 1: Factors and Levels for HPLC Robustness Testing

Factor Type	Factor	Nominal Level	Low Level (-1)	High Level (+1)
Chromatographic	Mobile phase pH	3.5	3.4	3.6
	Buffer concentration (mM)	20	18	22
	Organic modifier ratio (%)	35	33	37
	Flow rate (mL/min)	1.0	0.9	1.1
	Column temperature (°C)	35	33	37
	Detection wavelength (nm)	264	262	266
Environmental	Extraction time (min)	30	28	32
Instrumental	Different column batch	Batch A	-	Batch B

Factors are selected based on their potential impact on method performance and the likelihood of variation during method transfer. Quantitative factors are tested at symmetric intervals around the nominal level, representative of expected variations during method transfer [7]. The interval is typically determined as "nominal level ± k * uncertainty," where k ranges from 2 to 10 based on the estimated uncertainty in setting factor levels [7].

In some cases, asymmetric intervals may be appropriate, particularly when response behavior is not linear across the tested range. For instance, if the nominal detection wavelength corresponds to an absorbance maximum, testing only one extreme level may be more informative [7].

Experimental Design Selection

Two-level screening designs, particularly Plackett-Burman (PB) and fractional factorial (FF) designs, are most appropriate for robustness testing as they efficiently examine multiple factors with minimal experiments.

For the 8 factors listed in Table 1, a 12-experiment PB design is suitable, allowing examination of up to 11 effects (8 factors plus 3 dummy variables) [7]. This design is highly efficient for identifying the most influential factors affecting method performance.

Table 2: Plackett-Burman Design Matrix for 8 Factors

Experiment	pH	Buffer	Organic	Flow	Temp	Wavelength	Time	Column
1	+1	+1	+1	+1	+1	+1	+1	+1
2	-1	+1	-1	+1	+1	-1	-1	-1
3	-1	-1	+1	-1	+1	+1	-1	+1
4	+1	-1	-1	+1	-1	+1	+1	-1
5	+1	+1	-1	-1	+1	-1	+1	+1
6	+1	-1	+1	+1	-1	-1	+1	-1
7	-1	+1	+1	-1	-1	+1	+1	+1
8	-1	-1	-1	+1	+1	+1	-1	+1
9	-1	+1	-1	-1	-1	-1	+1	+1
10	+1	-1	-1	-1	-1	-1	-1	-1
11	-1	-1	+1	+1	+1	-1	-1	-1
12	+1	+1	+1	-1	-1	-1	-1	+1

Response Selection

Both quantitative assay responses and system suitability test (SST) parameters should be monitored during robustness testing:

Assay Responses:

• Percentage recovery of analytes
• Accuracy and precision measurements

System Suitability Parameters:

• Retention times and capacity factors
• Theoretical plate numbers
• Resolution between critical pairs
• Tailing or asymmetry factors
• Selectivity factors

For food applications, additional responses may include:

• Signal-to-noise ratio at the limit of quantification
• Selectivity against matrix interferences
• Extraction efficiency from the food matrix

Practical Implementation Protocol

Reagents and Materials

Table 3: Essential Research Reagent Solutions

Reagent/Material	Specification	Function in Analysis	Example from Literature
Ammonium acetate	HPLC grade, 20 mM concentration	Buffer component for mobile phase, maintains pH for consistent separation	Used at 20 mM concentration with pH 3.5 for MET/CAM analysis [20]
Methanol/Acetonitrile	HPLC grade	Organic modifier in mobile phase	Methanol used in 35% ratio with ammonium acetate buffer [20]
Phosphoric acid/Acetic acid	Analytical grade	pH adjustment of mobile phase	Glacial acetic acid used to adjust mobile phase to pH 3.5 [20]
Phenyl-hexyl column	250 mm × 4.6 mm, 5 µm	Stationary phase for chromatographic separation	Provided effective separation for compounds with different polarities [20]
Reference standards	Certified purity >95%	Quantification and identification of analytes	Metoclopramide and camylofin dihydrochloride from licensed suppliers [20]
Mobile phase filtration	0.45 µm nylon membrane	Removal of particulate matter	Filtered through 0.45 µm nylon membrane filters [20]

Equipment and Instrumentation

• HPLC system with UV-Vis or DAD detector, capable of precise flow control and temperature management
• pH meter with accuracy of ±0.01 units
• Analytical balance with readability of 0.1 mg
• Ultrasonic bath for mobile phase degassing
• Appropriate column oven for temperature control
• Sample preparation equipment: vortex mixer, centrifuge, ultrasonic extractor

Experimental Procedure

Step 1: Mobile Phase Preparation

• Prepare 20 mM ammonium acetate buffer by dissolving appropriate amount in HPLC-grade water
• Adjust pH to 3.5 using glacial acetic acid with constant monitoring
• Mix buffer with methanol in 65:35 ratio (aqueous:organic)
• Filter through 0.45 µm nylon membrane and degas using ultrasonic bath [20]

Step 2: Standard Solution Preparation

• Accurately weigh reference standards of target analytes
• Dissolve in appropriate solvent to prepare stock solutions
• Prepare working standards through serial dilution in mobile phase or matrix-matched solvent

Step 3: Experimental Sequence Execution

• Execute the experimental design in randomized order to minimize bias
• For each experimental condition, prepare mobile phase fresh daily
• Equilibrate column for at least 30 minutes under new conditions
• Inject system suitability test mixture to verify stability
• Inject six replicates of sample solutions for precision assessment
• Include quality control samples at low, medium, and high concentrations

Step 4: Data Collection

• Record retention times, peak areas, and peak symmetry for all analytes
• Calculate resolution between critical peak pairs
• Determine theoretical plate count for key peaks
• Quantify analyte concentrations using calibration curves

Step 5: Effect Calculation

• For each response, calculate the effect of each factor using the formula: Eₓ = (ΣY₊ - ΣY₋)/N Where Eₓ is the effect of factor X, ΣY₊ is the sum of responses when factor is at high level, ΣY₋ is the sum of responses when factor is at low level, and N is the number of experiments [7]

Workflow Visualization

Robustness Testing Implementation Workflow

Data Analysis and Interpretation

Statistical Analysis of Effects

The calculated effects must be statistically evaluated to distinguish significant effects from random variation. Two primary approaches are recommended:

Graphical Analysis:

• Normal probability plots: Significant effects deviate from the straight line formed by negligible effects
• Half-normal probability plots: Absolute effects are plotted against their cumulative probabilities

Statistical Significance Testing:

• Use dummy factors or algorithm-based methods to establish critical effect values
• The algorithm of Dong provides statistically derived critical values for effect significance [7]
• Effects exceeding the critical value are considered statistically significant

Establishing System Suitability Test Limits

Based on the robustness test results, appropriate system suitability test limits should be established:

• For significantly affected parameters, set tighter SST limits
• For robust parameters, wider limits may be acceptable
• SST limits should ensure method performance despite expected variations

Table 4: Example Robustness Test Results for HPLC Method

Factor	Variation	Retention Time RSD%	Peak Area RSD%	Resolution	Theoretical Plates
Mobile phase pH	3.4 - 3.6	1.2	1.8	1.5	1.3
Buffer concentration	18 - 22 mM	0.9	1.2	0.8	1.1
Organic modifier	33 - 37%	2.1	2.3	1.9	1.7
Flow rate	0.9 - 1.1 mL/min	3.2	1.1	2.4	1.5
Column temperature	33 - 37°C	1.1	0.9	0.7	0.8
Detection wavelength	262 - 266 nm	0.3	2.7	0.2	0.4
Acceptance criteria	-	≤2.0%	≤2.0%	≥1.5	≥2000

Decision Making and Method Adjustment

Based on the robustness test outcomes:

• If no significant effects are found, the method is considered robust
• If significant effects are identified:
  • Modify method parameters to reduce sensitivity to variations
  • Implement controls for critical parameters
  • Establish warning systems in the method procedure

Case Study: Robustness Testing in Food Analysis

Application to Bioactive Compound Analysis

A robustness test was applied to an HPLC method for trigonelline analysis in fenugreek seeds, a food ingredient with health benefits. The method utilized a NH2 column with acetonitrile:water (70:30, v/v) mobile phase, flow rate of 1.0 mL/min, and detection at 264 nm [71].

The robustness test examined variations in:

• Mobile phase composition (±2%)
• Flow rate (±0.1 mL/min)
• Column temperature (±2°C)
• Detection wavelength (±2 nm)

Results demonstrated that the method was robust for all parameters except mobile phase composition, which affected retention time and resolution. Consequently, the method procedure specified strict tolerances for mobile phase preparation (±1%) and included a system suitability requirement for resolution between trigonelline and a closely eluting compound.

Relationship Between Performance and Robustness

Experimental studies in biotechnology have demonstrated a trade-off between performance and robustness in biological systems [72]. While focused on microbial strains, these findings have implications for analytical methods: methods optimized for maximum performance under ideal conditions may show reduced robustness when faced with small variations.

This relationship underscores the importance of balanced method development that considers both optimal performance and acceptable robustness, particularly for food分析方法 that must perform reliably across diverse sample matrices and laboratory environments.

Robustness testing should not be conducted in isolation but fully integrated into the overall method validation framework. The sequential approach to validation should include:

Integration of Robustness Testing in Method Validation

This integrated approach ensures that:

• Robustness testing informs method refinement before full validation
• System suitability tests are based on experimentally determined critical parameters
• Method transfer includes knowledge of sensitive parameters requiring control
• Method performance remains consistent during routine use

Integrating robustness testing into the overall method validation framework is essential for developing reliable HPLC methods for food applications. By systematically evaluating the influence of method parameters through experimental design approaches, researchers can identify critical factors, establish appropriate system suitability criteria, and ensure method reliability during transfer and routine use. The protocol outlined in this application note provides a practical framework for implementing robustness testing that meets regulatory expectations and supports method credibility in food analysis.

In the context of a broader thesis on robustness testing for HPLC methods in food applications research, setting scientifically sound acceptance criteria is a fundamental requirement to ensure analytical method reliability. Robustness testing evaluates the capacity of an analytical procedure to remain unaffected by small, deliberate variations in method parameters and provides an indication of its reliability during normal usage [7]. For researchers, scientists, and drug development professionals, establishing meaningful acceptance criteria is critical for demonstrating method robustness as required by regulatory agencies like FDA, EMA, and ICH [39]. This application note provides detailed protocols and frameworks for defining statistically justified acceptance ranges for variable parameters in HPLC method robustness studies, with particular emphasis on food analytical applications.

Theoretical Framework

Regulatory Foundations and Definitions

The International Council for Harmonisation (ICH) defines robustness/ruggedness as "a measure of its capacity to remain unaffected by small but deliberate variations in method parameters and provides an indication of its reliability during normal usage" [7]. This definition establishes the foundational principle for setting acceptance criteria – they must reflect realistic variations expected during method transfer between laboratories, instruments, or analysts.

Within the Analytical Quality by Design (AQbD) framework, robustness is incorporated during method development rather than being evaluated solely at the validation step [73]. The Method Operable Design Region (MODR) is established as a robust working region where experimental variations do not cause the failure of any Critical Method Attributes (CMAs), providing a systematic approach to parameter range justification [16] [73].

Key Parameters Requiring Acceptance Criteria

Table 1: Critical Parameters for HPLC Robustness Assessment

Parameter Category	Specific Factors	Potential Impact on Results
Chemical Parameters	Mobile phase composition, pH changes, solvent composition, buffer concentration	Retention time shifts, peak shape alterations, resolution changes
Instrumental Parameters	Flow rate, column temperature, detection wavelength, injection volume	Retention time precision, peak area response, detection sensitivity
Environmental Factors	Temperature variations, humidity levels	Retention time stability, baseline noise
Operational Factors	Sample preparation techniques, column age (batch-to-batch variation), calibration standards	Analytical precision, accuracy, quantification reliability

Based on a systematic assessment of method robustness challenges, the key parameters listed in Table 1 require well-defined acceptance criteria to ensure method reliability [39]. These parameters represent the most common sources of variability in HPLC methods for food analysis, where complex matrices can amplify the effects of small methodological variations.

Experimental Design for Robustness Testing

Systematic Approach to Parameter Evaluation

A structured experimental design is essential for meaningful acceptance criteria setting. The robustness testing process involves multiple defined steps:

• Selection of factors and their levels: Identify critical method parameters through risk assessment and define appropriate testing ranges [7].
• Selection of experimental design: Choose appropriate statistical designs such as fractional factorial, Plackett-Burman, or Central Composite Designs [7] [73].
• Selection of responses: Define Critical Method Attributes (CMAs) such as resolution, tailing factor, theoretical plates, and retention time [16] [73].
• Definition of experimental protocol: Establish testing sequence, potentially using anti-drift sequences or randomized approaches [7].
• Estimation of factor effects: Calculate the influence of each parameter variation on method responses [7].
• Graphical and/or statistical analysis of effects: Interpret results using statistical tools and visualization methods [7].
• Drawing conclusions and establishing acceptance criteria: Define operational ranges based on statistical analysis and method capability [39] [7].

Statistical Design Selection

For robustness testing, two-level screening designs such as fractional factorial (FF) or Plackett-Burman (PB) designs are most appropriate, examining f factors in minimally f+1 experiments [7]. The selection of experimental design should be based on the number of factors being investigated and considerations for subsequent statistical interpretation of factor effects.

Table 2: Experimental Designs for Robustness Testing

Design Type	Number of Factors	Experiment Number	Applications
Plackett-Burman	f = N-1	N = multiple of 4	Screening primary factors with minimal experiments
Fractional Factorial	f ≤ N-1	N = power of 2	Studying factor effects and some interactions
Central Composite	Typically 2-5	Varies with center points	Response surface modeling and MODR establishment
Full Factorial	Typically 2-4	All factor combinations	Comprehensive evaluation of limited factors

For methods developed using AQbD principles, the MODR can be computed with uncertainty boundaries using confidence, prediction, or tolerance intervals with Monte Carlo simulations, providing a statistically robust foundation for acceptance criteria [73].

Defining Acceptance Criteria Ranges

Establishing Baseline Method Performance

Acceptance criteria must be derived from systematic robustness studies where parameters are deliberately varied and their impact on method responses is quantified. The criteria should reflect the method's capability to maintain performance within acceptable ranges despite expected operational variations.

Table 3: Exemplary Acceptance Criteria Based on Published Robustness Studies

Analytical Method	Varied Parameters	Tested Ranges	Acceptance Criteria	Reference
Favipiravir RP-HPLC	Column type, pH, solvent ratio	As per DoE	RSD < 2% for precision, accuracy, robustness	[16]
Mesalamine RP-HPLC	Not specified (slight variations)	As per robustness protocol	RSD < 2% for all method variations	[74]
Metoclopramide & Camylofin RP-HPLC	Flow rate, column temperature, mobile phase	Flow: ±0.1 mL/min, Temp: ±5°C	RSD < 2% for intra- and inter-day precision	[20]

The data in Table 3 demonstrates that for quantitative HPLC methods, a relative standard deviation (RSD) of <2% for precision measurements typically serves as an appropriate acceptance criterion for robustness [16] [74] [20]. This criterion should be applied to replicate measurements of system suitability parameters and analytical outcomes when method parameters are deliberately varied.

Parameter-Specific Acceptance Ranges

Based on systematic robustness testing approaches, the following parameter-specific acceptance criteria are recommended:

1. Mobile Phase Composition:

• Test Range: ±2-3% absolute for organic modifier比例
• Acceptance Criteria: Retention time RSD < 2%, resolution > 2.0 between critical pairs, peak asymmetry within 0.8-1.5
• Justification: Represents typical preparation variability between analysts and laboratories [39]

2. pH Variation:

• Test Range: ±0.1-0.2 pH units from nominal
• Acceptance Criteria: Retention time RSD < 2%, no elution order changes, resolution maintained > 2.0
• Justification: Reflects realistic buffer preparation capabilities in different laboratory environments [20]

3. Flow Rate:

• Test Range: ±0.1 mL/min for nominal 1.0 mL/min flow
• Acceptance Criteria: Retention time RSD < 2%, backpressure within instrument specifications
• Justification: Represents typical flow rate setting and calibration variations between instruments [20]

4. Column Temperature:

• Test Range: ±5°C from nominal temperature
• Acceptance Criteria: Retention time RSD < 2%, resolution maintained > 2.0, no significant selectivity changes
• Justification:
  • Accounts for typical column oven performance variations [20]
  • Based on documented robustness testing protocols [20]

5. Detection Wavelength:

• Test Range: ±3-5 nm for UV detection (asymmetric ranges if at λmax)
• Acceptance Criteria: Peak area RSD < 2%, signal-to-noise ratio maintained > 10 for quantitation
• Justification: Considers wavelength accuracy specifications of typical HPLC detectors [7]

Experimental Protocol for Robustness Assessment

Comprehensive Testing Workflow

Diagram 1: Robustness Assessment Workflow. This workflow illustrates the systematic process for establishing acceptance criteria, from initial definition of Analytical Target Profile (ATP) through Method Operable Design Region (MODR) validation.

Detailed Experimental Procedure

Materials and Equipment:

• HPLC system with binary pump, autosampler, column oven, and DAD or UV detector
• Chromatographic columns from at least two different batches or manufacturers
• HPLC-grade solvents and reagents
• Reference standards of target analytes
• pH meter with appropriate calibration buffers
• Analytical balance (readability 0.1 mg)

Procedure:

• System Suitability Setup: Establish baseline performance using nominal method conditions. Verify system meets minimum requirements (theoretical plates > 2000, tailing factor < 2.0, RSD < 1% for replicate injections).

• Experimental Execution:

  • Prepare mobile phases at upper and lower range limits for organic composition (±2%)
  • Adjust buffer pH to upper and lower limits (±0.1-0.2 units)
  • Set flow rate to defined extremes (±0.1 mL/min)
  • Adjust column temperature to tested range (±5°C)
  • For each parameter variation, inject six replicates of system suitability standard

• Data Collection:

  • Record retention times for all analytes
  • Calculate peak areas and areas for precision assessment
  • Measure resolution between critical peak pairs
  • Determine tailing factors and theoretical plates
  • Document signal-to-noise ratios for sensitivity assessment

• Data Analysis:

  • Calculate mean, standard deviation, and RSD for all quantitative responses
  • Perform statistical analysis (ANOVA) to identify significant effects
  • Create response surfaces for critical method attributes
  • Establish parameter ranges where all CMAs meet acceptance criteria

The Scientist's Toolkit

Table 4: Essential Research Reagent Solutions for Robustness Studies

Reagent/Material	Function in Robustness Assessment	Technical Specifications
HPLC-Grade Organic Solvents	Mobile phase component; variability source for composition studies	Low UV cutoff, purity >99.9%, minimal particulate matter
Buffer Salts	Mobile phase component; pH variability studies	HPLC grade, specified purity, low UV absorbance
pH Standard Solutions	Calibration of pH meters for buffer preparation	NIST-traceable, appropriate pH ranges (e.g., 2.0, 4.0, 7.0, 10.0)
Reference Standards	System suitability testing and method performance assessment	Certified purity, stability-under-stressed conditions
Chromatographic Columns	Column-to-column variability assessment	Multiple batches from single manufacturer; different manufacturers
Column Heater/Oven	Temperature control variability studies	Precise temperature control (±1°C), uniform heating
Membrane Filters	Sample preparation consistency	0.45 µm or 0.22 µm pore size, compatible with mobile phase

Data Interpretation and Regulatory Considerations

Statistical Analysis of Robustness Data

The evaluation of robustness study results requires both statistical analysis and practical assessment of chromatographic performance. Factor effects are calculated as the difference between the average responses when the factor is at its high level and the average responses when it is at its low level [7]. These effects can be visualized using normal probability plots or half-normal probability plots to distinguish significant effects from random variation [7].

For methods developed using AQbD principles, the MODR represents the multidimensional combination of analytical factor ranges where the method meets all CMA requirements [73]. The MODR should be established using prediction intervals or tolerance intervals to incorporate uncertainty, ensuring robustness throughout the defined operational space [73].

Documentation and Regulatory Submission

Robustness data should be comprehensively documented in method validation reports, including:

• Experimental design and justification
• Raw data for all parameter variations
• Statistical analysis of factor effects
• Derived acceptance criteria with scientific justification
• MODR definition with supporting data

Regulatory agencies expect that "the accuracy, sensitivity, specificity, and reproducibility of test methods employed by the firm shall be established and documented" [47]. The acceptance criteria derived from robustness studies provide essential evidence of method reproducibility and form the basis for system suitability test limits in routine application.

Setting meaningful acceptance criteria for HPLC method robustness requires a systematic, statistically-driven approach that evaluates the impact of parameter variations on critical method attributes. By implementing the protocols outlined in this application note, researchers can establish scientifically justified acceptance ranges that ensure method reliability during technology transfer and routine use in food analysis laboratories. The integration of robustness testing within the AQbD framework, with establishment of a well-defined MODR, provides the most comprehensive approach to demonstrating method robustness for regulatory submissions and quality control applications.

Robustness testing represents a critical validation parameter in analytical chemistry, defined as the capacity of an analytical procedure to remain unaffected by small, deliberate variations in method parameters [7]. In the context of high-performance liquid chromatography (HPLC) method development for food and pharmaceutical applications, demonstrating robustness provides an indication of the method's reliability during normal usage and is essential for regulatory compliance and successful method transfer between laboratories [7] [10]. Traditionally, robustness testing has been performed using univariate approaches, where one factor is varied at a time while keeping others constant. However, with increasing analytical complexity and the need for more comprehensive method understanding, multivariate approaches utilizing experimental design (DoE) have gained prominence, particularly following the implementation of Analytical Quality by Design (AQbD) principles [16] [17].

This article presents a systematic comparison of these two methodological frameworks, focusing on their application in HPLC method validation for food analysis. The fundamental distinction between these approaches lies in their experimental strategy: univariate methods isolate individual factor effects, while multivariate methods evaluate interactions between multiple parameters simultaneously, providing a more comprehensive understanding of the method's operational boundaries [7] [75].

Theoretical Foundations and Definitions

Robustness in Analytical Method Validation

The International Conference on Harmonisation (ICH) defines robustness as "a measure of the capacity of an analytical procedure to remain unaffected by small but deliberate variations in method parameters and provides an indication of its reliability during normal usage" [7]. For HPLC methods, this typically involves testing the impact of variations in factors such as mobile phase composition, pH, column temperature, flow rate, and detection wavelength on critical method responses including retention time, peak area, resolution, tailing factor, and theoretical plate count [7] [10].

Univariate Approach Fundamentals

The univariate approach, also known as one-factor-at-a-time (OFAT), involves systematically varying a single method parameter while maintaining all others at their nominal (optimized) levels. This traditional method allows for straightforward interpretation of results and is simple to implement without specialized statistical software [7]. However, its major limitation is the inability to detect interaction effects between factors, which may lead to an incomplete understanding of the method's behavior across its operational range [7].

Multivariate Approach Fundamentals

Multivariate approaches utilize statistical experimental design (Design of Experiments, DoE) to vary multiple parameters simultaneously according to a predefined matrix. This strategy allows for efficient exploration of the factor space and enables the detection and quantification of interaction effects between parameters [16] [75]. Common designs employed in robustness testing include full factorial, fractional factorial, and Plackett-Burman designs, each offering different balances between experimental effort and information gain [7].

Comparative Analysis: Experimental Design and Implementation

Experimental Design Considerations

Table 1: Comparison of Experimental Design Characteristics

Characteristic	Univariate Approach	Multivariate Approach
Experimental Strategy	One factor varied at a time	Multiple factors varied simultaneously
Number of Experiments	f + 1 (where f = number of factors)	Varies by design (e.g., 2f for full factorial)
Interaction Detection	Not detectable	Fully detectable
Statistical Basis	Simple comparisons	Complex models (ANOVA, regression)
Resource Requirements	Lower initial investment	Higher initial investment
Regulatory Alignment	Traditional acceptance	AQbD principles, ICH Q14
Software Dependence	Minimal	Essential for design and analysis

Application in HPLC Method Development

In HPLC method development for food analysis, both approaches have demonstrated utility in different contexts. A univariate robustness study for a domiphen bromide HPLC method evaluated factors including mobile phase ratio, flow rate, and column temperature individually, confirming method performance remained within acceptance criteria across specified ranges [17]. Similarly, a favipiravir quantification method employed univariate testing to verify robustness with RSD values <2% [16].

In contrast, multivariate approaches have enabled more comprehensive method understanding. In the development of an RP-HPLC method for simultaneous estimation of metoclopramide and camylofin, response surface methodology (RSM) was employed to optimize multiple factors simultaneously, including buffer concentration, pH, and organic modifier ratio [20]. This approach established a design space where method performance criteria were consistently met, demonstrating enhanced method understanding compared to univariate testing.

Quantitative Comparison of Outcomes

Table 2: Performance Comparison in HPLC Applications

Performance Metric	Univariate Results	Multivariate Results
Factor Effects Identification	Isolated main effects	Main effects + interactions
Design Space Definition	Limited operational ranges	Comprehensive MODR (Method Operable Design Region)
Method Understanding	Basic robustness confirmation	Enhanced mechanistic understanding
Risk Assessment	Qualitative	Quantitative, risk-based
Method Transfer Success	Variable between laboratories	Higher success rate
Regulatory Submission	Traditional data packages	Enhanced data packages under AQbD

Advanced multivariate modeling approaches have further expanded robustness testing capabilities. Multidimensional modeling incorporating gradient time, temperature, and pH as parameters has enabled prediction of chromatographic behavior across a wide operational space, facilitating identification of robust method conditions [75]. Such models have proven valuable in column interchangeability studies and batch-to-batch reproducibility assessments, where traditional univariate approaches would be insufficient to characterize the complex relationships between method parameters [75].

Experimental Protocols

Protocol 1: Univariate Robustness Testing for HPLC Methods

Principle: This protocol evaluates the impact of individual HPLC method parameters by varying one factor at a time while maintaining others constant, assessing effects on critical chromatographic responses [7] [10].

Materials and Reagents:

• HPLC system with DAD or UV-Vis detector
• Analytical column (as specified in method)
• Mobile phase components (HPLC grade)
• Reference standards and test samples
• pH meter, volumetric glassware

Experimental Procedure:

• Factor Selection and Level Definition:

  • Identify critical method parameters (typically 5-7 factors) based on risk assessment
  • Define high and low levels for each factor (±1% for organic modifier composition, ±0.1 units for pH, ±5°C for column temperature, ±0.1 mL/min for flow rate) [10]
  • Establish acceptance criteria for system suitability parameters

• Baseline Chromatographic Analysis:

  • Perform analysis using nominal method conditions
  • Verify system suitability requirements are met
  • Record retention times, peak areas, resolution, tailing factors, and theoretical plates

• Factor Variation Sequence:

  • Prepare mobile phase with organic modifier at low level (-1%), analyze samples in triplicate
  • Prepare mobile phase with organic modifier at high level (+1%), analyze samples in triplicate
  • Adjust pH to low level (-0.1 units), analyze samples in triplicate
  • Continue sequence for all identified factors, returning to nominal conditions between variations

• Data Analysis:

  • Calculate mean and relative standard deviation (RSD) for each response variable
  • Compare results against predefined acceptance criteria
  • Document any trends or failures

Data Interpretation: The method is considered robust if all system suitability parameters remain within acceptance criteria across all tested factor levels. Any parameter showing significant sensitivity to variations should be identified as a critical method parameter and controlled through system suitability tests or method precautions [7].

Protocol 2: Multivariate Robustness Testing Using Experimental Design

Principle: This protocol employs statistical experimental design to vary multiple method parameters simultaneously, enabling detection of interaction effects and definition of a method operable design region (MODR) [16] [75].

Materials and Reagents:

• HPLC system with DAD or UV-Vis detector
• Multiple columns from different batches or manufacturers (if column type is a factor)
• Mobile phase components (HPLC grade)
• Reference standards and test samples
• Experimental design software (MODDE, Design-Expert, etc.)

Experimental Procedure:

• Factor Selection and Experimental Design:

  • Select 4-8 critical method parameters based on prior knowledge and risk assessment
  • Choose appropriate experimental design (Plackett-Burman for screening, fractional factorial for response modeling)
  • Define factor levels representing realistic variations expected during method use

• Experimental Sequence Execution:

  • Randomize experimental run order to minimize bias
  • Prepare mobile phases and solutions according to design specifications
  • Execute chromatographic analyses following randomized sequence
  • Record all response variables (retention times, resolution, peak area, etc.)

• Model Development and Validation:

  • Use multiple linear regression to develop mathematical models relating factors to responses
  • Assess model validity using statistical indicators (R², Q², model lack-of-fit)
  • Perform analysis of variance (ANOVA) to identify significant factors and interactions

• Design Space Visualization and MODR Establishment:

  • Create contour plots and response surfaces to visualize factor-effects relationships
  • Identify region in factor space where all method criteria are met (MODR)
  • Verify MODR boundaries with additional confirmation experiments

Data Interpretation: The method is considered robust when a sufficient MODR exists where all critical quality attributes meet acceptance criteria. Factor interactions identified through the model should be considered when establishing method control strategies [16] [75].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Reagents and Materials for HPLC Robustness Studies

Item	Function in Robustness Testing	Application Notes
Inertsil ODS-3 C18 Column	Stationary phase for separation	250 mm × 4.6 mm, 5 μm particle size; provides reproducible retention [16]
Ammonium Acetate Buffer	Mobile phase component for pH control	20 mM concentration, pH adjusted with acetic acid; maintains stable pH conditions [20]
Acetonitrile (HPLC Grade)	Organic modifier in reversed-phase HPLC	Variations of ±1% v/v typically tested; primary driver of retention time changes [10]
Perchloric Acid	Ion-pairing reagent and pH modifier	0.0116 M concentration; improves peak shape for basic compounds [17]
Methanol (HPLC Grade)	Alternative organic modifier	Used in method development for comparison with acetonitrile; different selectivity [20]
Design of Experiments Software	Statistical design and data analysis	MODDE 13 Pro, Design Expert; enables multivariate experimental design and MODR determination [16]

Workflow and Decision Pathways

The following workflow diagram illustrates the logical decision process for selecting between univariate and multivariate robustness testing approaches based on method requirements and constraints:

HPLC Robustness Assessment Workflow

The selection between univariate and multivariate robustness testing approaches depends on multiple factors, including method complexity, criticality, regulatory requirements, and available resources. Univariate methods offer simplicity and efficiency for straightforward methods with limited factors, while multivariate approaches provide comprehensive method understanding and are essential for identifying factor interactions. The implementation of AQbD principles in pharmaceutical and food analysis has accelerated adoption of multivariate approaches, enabling development of more robust and well-characterized analytical methods [16] [17].

For HPLC methods in food applications, where matrix complexity and regulatory scrutiny continue to increase, multivariate robustness testing provides superior method understanding and facilitates more successful method transfer and lifecycle management. As demonstrated through case studies in favipiravir, metoclopramide/camylofin, and domiphen bromide analysis, both approaches can successfully demonstrate method robustness when appropriately applied to suitable analytical challenges [16] [20] [17].

For researchers and scientists in food and pharmaceutical development, a regulatory submission is a critical milestone that demands rigorous analytical method validation. Within this framework, robustness testing serves as a cornerstone, demonstrating that a high-performance liquid chromatography (HPLC) method remains unaffected by small, deliberate variations in method parameters under normal usage [3]. In food applications, where matrix complexity is high, proving method reliability is essential for gaining regulatory approval and ensuring consistent product quality and safety [13]. This application note provides a detailed protocol for designing, executing, and documenting a comprehensive robustness study, equipping you to build a defensible regulatory submission package.

Core Principles of HPLC Robustness Testing

Definitions and Regulatory Importance

Robustness is defined as "a measure of [an analytical procedure's] capacity to remain unaffected by small but deliberate variations in procedural parameters listed in the documentation" [3]. For HPLC methods in food analysis, this evaluates the method's resilience against minor fluctuations in conditions such as mobile phase pH, column temperature, and flow rate that may occur between laboratories, analysts, or instruments [3] [10].

Investigating robustness during method development, rather than after formal validation, is a proactive strategy that identifies critical parameters early. This allows for method refinement before full validation, saving significant time and resources while strengthening the regulatory defense package [3].

Key Parameters for Robustness Evaluation in Food Analysis

The table below summarizes critical HPLC parameters and typical variations for robustness testing in food analysis methods.

Table 1: Key Parameters and Typical Variations for HPLC Robustness Testing

Category	Parameter	Typical Variation Range	Impact on Separation
Mobile Phase	Organic Solvent Ratio (%B)	± 1-2% [10]	Major impact on retention time and resolution; a 10% change can alter retention by a factor of ~3 [10].
	Buffer pH	± 0.1-0.2 units [3]	Critical for ionizable analytes; affects ionization state and retention.
	Buffer Concentration	± 2-5 mM [3]	Can impact peak shape and retention of ionic analytes.
Chromatographic Hardware	Column Temperature	± 2-5 °C [20]	Affects retention time and efficiency; can alter selectivity.
	Flow Rate	± 0.1 mL/min [20] [10]	Directly impacts retention time and backpressure.
	Detection Wavelength	± 2-3 nm [3]	Affects sensitivity and signal-to-noise ratio for UV/Vis detection.
Column Characteristics	Column Lot/Brand	Different lots or equivalent brands [3]	Assesses reproducibility of stationary phase chemistry.

Experimental Protocol for Robustness Testing

Designing the Robustness Study

A robust experimental design is crucial for efficiently evaluating multiple parameters. A univariate approach (one variable at a time) is simple but inefficient and may miss parameter interactions. Multivariate screening designs are the preferred statistical approach [3].

• Full Factorial Designs: Evaluate all possible combinations of factors at two levels (high/low). Suitable for investigating a small number of factors (e.g., 3 factors require 2³=8 runs) [3].
• Fractional Factorial Designs: A carefully chosen subset of a full factorial design. Ideal for evaluating 4 or more factors, as it significantly reduces the number of required experimental runs while still revealing main effects [3].
• Plackett-Burman Designs: Highly efficient screening designs for identifying the most influential factors from a large set, using an experimental run number that is a multiple of four [3].

The following diagram illustrates the workflow for selecting an appropriate experimental design:

Step-by-Step Methodology

Step 1: Factor and Level Selection

• Identify critical method parameters from Table 1 that are most likely to impact separation (e.g., %B, pH, temperature) [10].
• Define realistic, deliberate variation levels for each parameter based on expected operational variances (e.g., ±0.1 for pH, ±1% for organic modifier) [10].

Step 2: Experimental Execution

• Prepare mobile phases and standards according to the method specification.
• Perform chromatographic runs as per the selected experimental design (e.g., full factorial). Use a fixed sample to ensure observed changes are due to parameter variations.
• For each run, record critical performance criteria: retention time, peak area, tailing factor, and resolution between the critical peak pair [10].

Step 3: Data Analysis and Interpretation

• Analyze data using statistical methods (e.g., Analysis of Variance - ANOVA) to determine which parameters significantly affect each response [20] [17].
• A parameter is considered non-influential if variations within the tested range cause no statistically significant change in the system suitability criteria.
• If a parameter is found to be critical, the method may be refined to operate in a less sensitive region, or system suitability criteria may be tightened to control that parameter [3].

Case Study: Robustness Testing in Practice

A recent study developing a stability-indicating RP-HPLC method for Domiphen Bromide provides an excellent model [17]. The researchers employed a Quality by Design (QbD) approach with a 2³ full factorial design to optimize and demonstrate robustness.

• Factors & Levels: They investigated three critical parameters: acetonitrile ratio (±2%), flow rate (±0.1 mL/min), and column temperature (±2°C).
• Analysis: Statistical analysis (ANOVA) confirmed that the method remained accurate and precise across all tested factor-level combinations, successfully establishing a robust "method operable design region" (MODR) [17].
• Outcome: This systematic approach provided a high level of confidence in the method's reliability and formed a defensible part of the overall validation package.

The Scientist's Toolkit: Essential Research Reagent Solutions

The following table lists key reagents, materials, and software solutions critical for conducting successful HPLC robustness studies.

Table 2: Essential Research Reagent Solutions for Robustness Testing

Item	Function/Application	Considerations for Robustness Testing
HPLC Grade Solvents (Acetonitrile, Methanol)	Mobile phase components.	Use high-purity solvents from a consistent supplier to minimize baseline noise and variability.
Buffer Salts & pH Modifiers (e.g., Ammonium acetate, phosphate salts, acetic acid, perchloric acid)	Mobile phase modifiers for controlling pH and ionic strength.	Prepare fresh daily; precise pH adjustment is critical for robustness of ionizable analytes [20] [17].
Chromatographic Columns (e.g., C18, Phenyl-Hexyl)	Stationary phase for analyte separation.	Test columns from at least two different manufacturing lots to assess reproducibility [3].
Standard Reference Materials	Calibration and system suitability testing.	Use highly characterized, pure materials to ensure the accuracy of all measurements.
Design of Experiments (DoE) Software (e.g., MODDE, Design Expert)	Statistical design and analysis of robustness studies.	Essential for efficient experimental design (e.g., factorial designs) and data analysis using ANOVA [16] [20].
Method Scouting & Modeling Software (e.g., ACD/LC Simulator, DryLab)	In-silico prediction of chromatographic behavior under different conditions.	Allows for virtual robustness testing by modeling the impact of parameter changes, reducing lab work [10].

The following workflow summarizes the integration of these components in a robustness study:

Compiling the Regulatory Defense Package

The final defense package must present the robustness data clearly and conclusively. Structure this section to include:

• Introduction: A brief statement on the purpose and design of the study.
• Experimental Design: A clear description of the selected factors, their ranges, and the design type (e.g., full factorial).
• Results Summary: A table presenting the key responses (e.g., retention time, resolution) under the varied conditions against the system suitability limits.
• Statistical Analysis: A summary of the ANOVA or equivalent analysis, highlighting that no single parameter caused a statistically significant or clinically relevant change in the method's performance.
• Conclusion: A definitive statement that the method is robust for its intended use within the defined operational ranges.

The integration of Analytical Quality by Design (AQbD) principles with Green Analytical Chemistry (GAC) represents a transformative approach to developing robust, sustainable, and effective analytical methods for food and pharmaceutical analysis. This paradigm shift addresses the dual challenges of ensuring methodological reliability while minimizing environmental impact. Conventional reversed-phase high-performance liquid chromatography (RP-HPLC) methods often rely on hazardous organic solvents, generate substantial waste, and consume significant energy, creating substantial environmental concerns [13]. Within the context of a broader thesis on robustness testing for HPLC methods in food applications, this case study demonstrates how the systematic AQbD framework can be applied to develop an eco-friendly RP-HPLC method for quantifying synthetic food colorants, specifically using Sunset Yellow FCF (E110) as a model analyte [76]. The AQbD approach ensures method robustness by proactively identifying and controlling critical method parameters, while GAC principles guide the replacement of hazardous solvents with safer alternatives like ethanol, reducing the method's environmental footprint without compromising analytical performance [76].

Theoretical Background

Fundamentals of Analytical Quality by Design (AQbD)

The AQbD framework represents a systematic, risk-based approach to analytical method development that emphasizes proactive understanding rather than reactive testing. This paradigm shift ensures methods are robust and reliable across their entire lifecycle under various conditions. The core components of AQbD include defining the Analytical Target Profile (ATP), identifying Critical Method Attributes (CMAs), conducting risk assessment to pinpoint Critical Method Parameters (CMPs), establishing a method operable design region, and implementing continual method verification [76] [17]. For HPLC methods in food analysis, the ATP typically specifies requirements for accuracy, precision, sensitivity, and selectivity, while CMAs often include critical resolution, peak asymmetry, and retention time. The design space is mathematically modeled through experimental designs like central composite design, ensuring method robustness within defined parameter ranges [76].

Principles of Green Analytical Chemistry

Green Analytical Chemistry provides a structured framework for reducing the environmental impact of analytical practices through 12 foundational principles [13]. These include minimizing waste generation, selecting safer solvents and reagents, reducing energy consumption, implementing reagent-free or miniaturized methods, and applying multi-analyte approaches [13]. In chromatographic method development, these principles translate to replacing hazardous solvents like acetonitrile and methanol with eco-friendly alternatives such as ethanol, reducing sample and solvent volumes, minimizing waste generation, and employing energy-efficient instrumentation [13] [76]. The emerging concept of White Analytical Chemistry (WAC) further expands this paradigm by seeking an optimal balance between analytical performance (red), environmental sustainability (green), and practical applicability (blue), with the ideal "white" method harmonizing all three dimensions [13] [76].

Case Study: Green RP-HPLC Method for Sunset Yellow in Food Samples

Method Development Strategy

This case study details the development of an AQbD-driven green RP-HPLC method for quantifying Sunset Yellow FCF (E110), a synthetic azo dye used in various food products [76]. The method employed green ultrasound-assisted extraction for sample preparation and used ethanol as a safer solvent alternative in the mobile phase, aligning with GAC principles [76]. The chromatographic separation was optimized using a rotatable central composite design (rCCD) to model the relationship between critical method parameters and responses, establishing a robust design space for method operation [76].

Table 1: Analytical Target Profile (ATP) for Sunset Yellow RP-HPLC Method

Parameter	Target Requirement	Justification
Analytical Technique	RP-HPLC with UV detection	Suitable for quantification of synthetic dyes in food matrices
Detection Wavelength	510 nm	Maximum absorbance of Sunset Yellow FCF
Linearity Range	To be determined experimentally	Cover expected concentration range in food samples
Accuracy	98-102%	Compliance with regulatory requirements for food analysis
Precision	RSD < 2%	Ensure reliable quantification in routine analysis
Selectivity	Baseline resolution from potential interferents	Specific quantification in complex food matrices

Experimental Protocol

Materials and Reagents

• Reference standard: Sunset Yellow FCF (high purity grade)
• Solvents: Ethanol (HPLC grade), acetate buffer
• Samples: Commercial food products containing Sunset Yellow FCF
• Equipment: HPLC system with UV-Vis detector, Phenomenex C18 column (250 mm × 4.6 mm, 5 μm), ultrasonic bath, pH meter

Chromatographic Conditions

• Column: Phenomenex C18 (250 mm × 4.6 mm, 5 μm)
• Mobile phase: Ethanol-acetate buffer (34.4:65.6, v/v)
• Flow rate: 1.1 mL/min
• Detection wavelength: 510 nm
• Injection volume: 20 μL
• Temperature: Ambient

Sample Preparation Protocol

• Homogenization: Thoroughly homogenize food samples to ensure representative sampling.
• Weighing: Accurately weigh approximately 1 g of sample into a 50 mL centrifuge tube.
• Extraction: Add 10 mL of ethanol and extract using ultrasound-assisted extraction for 15 minutes at controlled temperature.
• Centrifugation: Centrifuge at 4000 rpm for 10 minutes to separate solid residues.
• Filtration: Filter the supernatant through a 0.45 μm membrane filter.
• Dilution: Appropriately dilute with mobile phase to fit the calibration range.

Standard Solution Preparation

• Stock solution: Accurately weigh 10 mg of Sunset Yellow reference standard and transfer to a 10 mL volumetric flask. Dissolve and dilute to volume with ethanol to obtain 1000 μg/mL stock solution.
• Working standards: Prepare serial dilutions from stock solution using mobile phase to create calibration standards covering the expected concentration range.

The following workflow diagram illustrates the comprehensive AQbD approach applied in this case study:

AQuB Workflow for HPLC Method Development

Optimization Using Experimental Design

The method optimization employed a rotatable central composite design (rCCD) to systematically evaluate the effects of critical method parameters on chromatographic responses [76]. The experimental design and resulting optimization are visualized below:

DoE Optimization of HPLC Parameters

Method Validation

The developed method was validated according to ICH guidelines, demonstrating satisfactory performance characteristics across all validation parameters [76].

Table 2: Method Validation Results

Validation Parameter	Results	Acceptance Criteria
Linearity Range	1-100 μg/mL	R² > 0.999
Precision (RSD)	< 2%	≤ 2%
Accuracy (% Recovery)	98.8-99.76%	98-102%
LOD	0.373 μg/mL	-
LOQ	1.132 μg/mL	-
Specificity	No interference from excipients or degradation products	Baseline resolution
Robustness	Insignificant variation with deliberate parameter changes	Consistent retention time and resolution

Greenness and Whiteness Assessment

The environmental sustainability and practical applicability of the method were comprehensively evaluated using multiple assessment tools [13] [76].

Table 3: Greenness and Whiteness Assessment Results

Assessment Tool	Score/Result	Interpretation
Analytical Eco-Scale	High score	Low environmental impact
GAPI	Favorable color-coded result	Green aspects dominate
AGREE Metric	High score (close to 1)	Strong alignment with GAC principles
BAGI (Blueness)	High score	Excellent practical applicability
RGB 12 Algorithm (Whiteness)	High whiteness score	Optimal balance of red (analytical performance), green (environmental), and blue (practical) attributes

Application Notes and Protocols

Essential Research Reagent Solutions

The successful implementation of this green AQbD-based RP-HPLC method requires specific reagents and materials that align with both quality and sustainability principles.

Table 4: Essential Research Reagent Solutions

Reagent/Material	Function	Green Alternative	Traditional Hazardous Option
Ethanol (HPLC grade)	Mobile phase component	Safer, biodegradable solvent	Acetonitrile, Methanol
Acetate Buffer	Mobile phase modifier	Low toxicity aqueous component	Ion-pairing reagents
Phenomenex C18 Column	Stationary phase for separation	-	-
Ultrasonic Bath	Green extraction technique	Reduced solvent consumption	Soxhlet extraction
Ethanol-Water Mixture	Sample preparation diluent	Safer, biodegradable	Chloroform-Hexane mixtures

Detailed Protocol for Method Implementation

Mobile Phase Preparation

• Acetate Buffer Preparation: Dissolve appropriate amounts of sodium acetate and acetic acid in water to obtain 0.05 M buffer at pH 4.5.
• Mobile Phase Mixture: Accurately measure 344 mL of ethanol and 656 mL of acetate buffer.
• Mixing and Degassing: Combine solvents in a clean vessel and mix thoroughly. Degas by sonication for 15 minutes or by sparging with helium gas.
• Filtration: Filter through 0.45 μm nylon membrane under vacuum.

System Suitability Testing

• Preparation of System Suitability Solution: Prepare a solution containing Sunset Yellow at target concentration (approximately 10 μg/mL) in mobile phase.
• Injection Sequence: Inject six replicates of the system suitability solution.
• Acceptance Criteria:
  • Retention time RSD ≤ 1%
  • Peak area RSD ≤ 2%
  • Tailing factor ≤ 1.5
  • Theoretical plates ≥ 2000

Calibration Curve Construction

• Standard Preparation: Prepare at least five standard solutions spanning the concentration range from 1 to 100 μg/mL.
• Analysis Sequence: Inject each standard in triplicate following system suitability tests.
• Linear Regression: Plot average peak area versus concentration and perform linear regression analysis.
• Acceptance Criteria: Correlation coefficient (R²) ≥ 0.999, y-intercept not significantly different from zero.

Troubleshooting Guide

Issue	Potential Cause	Solution
Peak Tailing	Silanol interactions	Adjust buffer pH or concentration
Retention Time Drift	Mobile phase evaporation or column temperature fluctuation	Prepare fresh mobile phase daily, ensure column thermostatting
Baseline Noise	Contaminated mobile phase or detector lamp issues	Use high-purity reagents, replace mobile phase regularly
Poor Resolution	Incorrect organic modifier ratio	Fine-tune ethanol percentage within design space

Discussion

Interpreting Assessment Results

The comprehensive greenness assessment using multiple metrics provides strong evidence for the environmental advantages of the AQbD-developed method [13] [76]. The high scores on the Analytical Eco-Scale and AGREE metric reflect significant reductions in hazardous solvent consumption and waste generation compared to conventional methods [76]. The BAGI evaluation demonstrates excellent practical applicability, with high scores in throughput, cost-effectiveness, and operator safety [13] [76]. Most importantly, the RGB 12 algorithm confirmed the method's "whiteness" by achieving an optimal balance between analytical performance, environmental sustainability, and practical utility [76]. This holistic assessment approach moves beyond traditional method validation to provide a comprehensive evaluation framework that aligns with modern analytical chemistry priorities.

Implications for Food Analysis Methods

The successful application of AQbD to green RP-HPLC method development has significant implications for food analysis laboratories. The systematic approach enhances method robustness by proactively identifying and controlling critical parameters, reducing the need for method adjustments during transfer between laboratories [76] [17]. The replacement of traditional solvents like acetonitrile with ethanol addresses both sustainability goals and practical concerns regarding solvent cost, availability, and disposal [13] [76]. Furthermore, the integration of green extraction techniques like ultrasound-assisted extraction demonstrates how multiple stages of the analytical workflow can be optimized for minimal environmental impact [76]. This case study provides a transferable template for developing sustainable analytical methods for other food additives, contaminants, and nutrients, supporting the food industry's transition toward more environmentally responsible quality control practices.

This case study demonstrates the successful application of Analytical Quality by Design principles to develop a green RP-HPLC method for quantifying Sunset Yellow in food samples. The systematic AQbD approach enabled method optimization within an environmentally conscious framework, resulting in a robust, reproducible, and sustainable analytical procedure. Key achievements include the replacement of hazardous solvents with ethanol, implementation of green ultrasound-assisted extraction, and verification of method whiteness through comprehensive assessment tools. The method delivers satisfactory analytical performance while aligning with green chemistry principles, offering a viable template for developing sustainable analytical methods in food quality control. Future work should focus on expanding this approach to multi-analyte methods for related food additives and contaminants, further enhancing the efficiency and sustainability of food analysis.

Conclusion

Robustness testing is not merely a regulatory checkbox but a fundamental component of developing reliable HPLC methods for food analysis. By systematically assessing how methods perform under small, deliberate variations, scientists can ensure data integrity, facilitate successful method transfer between laboratories, and maintain consistent quality control. The future of robustness testing lies in the increased adoption of quality-by-design principles, multivariate statistical approaches, and AI-driven automation, which collectively enhance predictive method optimization. As food matrices grow more complex and regulatory scrutiny intensifies, a proactive and thorough approach to robustness will be paramount for advancing food safety, supporting product claims, and protecting public health through trustworthy analytical data.

References

• 1. What is the Difference Between Ruggedness And ... [https://pharmaguru.co/difference-between-ruggedness-and-robustness/]
• 2. Robustness and Ruggedness Testing in Analytical Chemistry [https://www.labmanager.com/robustness-and-ruggedness-testing-in-analytical-chemistry-34247]
• 3. Method Validation and Robustness | LCGC International [https://www.chromatographyonline.com/view/method-validation-and-robustness]
• 4. Guidance for robustness/ruggedness tests in method ... [https://www.sciencedirect.com/science/article/abs/pii/S073170850000529X]
• 5. Validation of the High-Performance Liquid ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11699575/]
• 6. HPLC Method Development and Validation for ... [https://www.pharmtech.com/view/hplc-method-development-and-validation-pharmaceutical-analysis]
• 7. Robustness Tests | LCGC International [https://www.chromatographyonline.com/view/robustness-tests]
• 8. ICH publishes Training Materials on Q2(R2) and Q14 [https://www.gmp-compliance.org/gmp-news/ich-publishes-training-materials-on-q2r2-and-q14]
• 9. Q1 Stability Testing of Drug Substances and Drug Products [https://www.fda.gov/regulatory-information/search-fda-guidance-documents/q1-stability-testing-drug-substances-and-drug-products]
• 10. Implementing Robustness Testing for HPLC Methods [https://www.sepscience.com/implementing-robustness-testing-for-hplc-methods-6978]
• 11. Recent advances in high-throughput green analytical ... [https://www.sciencedirect.com/science/article/pii/S2772577425000886]
• 12. Development of a reverse-phase HPLC method for the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12562788/]
• 13. Green Approaches in High-Performance Liquid ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12430196/]
• 14. Analytical Method Transfer: Best Practices and Guidelines [https://www.labmanager.com/analytical-method-transfer-best-practices-and-guidelines-34130]
• 15. Tools for Chromatographic Success: Supporting Data Quality [https://www.waters.com/blog/tools-for-chromatographic-success-three-ways-to-support-data-quality-in-routine-analysis/?srsltid=AfmBOorn7WObKpdp3SsDG4vXaakRBfayic8FnSgjVEy4N4MG-28O2S_g]
• 16. Development and validation of a novel isocratic RP-HPLC ... [https://www.sciencedirect.com/science/article/pii/S092809872500274X]
• 17. Robust RP-HPLC Method for the Analysis of Domiphen ... [https://link.springer.com/article/10.1007/s12247-025-10074-9]
• 18. Cross-Validation of Analytical Method: What It Is, How & ... [https://pharmaguru.co/cross-validation-in-analyticalytical-methods/]
• 19. Development and Validation of HPLC-DAD Method for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7466259/]
• 20. Developing and Validating a Robust RP-HPLC Method for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12543500/]
• 21. Robustness evaluation in analytical methods optimized ... [https://www.sciencedirect.com/science/article/abs/pii/S0026265X16303599]
• 22. Don't Get Lost in Your HPLC Method Development [https://www.thermofisher.com/blog/analyteguru/don-t-get-lost-in-your-hplc-method-development/]
• 23. High-Temperature Liquid Chromatography [https://www.chromatographyonline.com/view/high-temperature-liquid-chromatography]
• 24. Optimizing HPLC Pressure: Real Solutions for Better ... [https://www.sepscience.com/optimizing-hplc-pressure-real-solutions-for-better-performance-10610]
• 25. 2025 HPLC Column and Accessories Review [https://www.chromatographyonline.com/view/innovations-in-liquid-chromatography-2025-hplc-column-and-accessories-review]
• 26. New HPLC, MS, and CDS Products from 2024–2025 [https://www.chromatographyonline.com/view/new-hplc-ms-and-cds-products-from-2024-2025-a-brief-review]
• 27. High-performance liquid chromatography with post-column ... [https://www.sciencedirect.com/science/article/abs/pii/S0308814625032832]
• 28. Multi-matrix HPLC investigation of preservatives employing ... [https://www.sciencedirect.com/science/article/abs/pii/S0278691525000493]
• 29. Investigation of the HPLC response of NSAIDs by fractional ... [https://www.sciencedirect.com/science/article/abs/pii/S0731708509005056]
• 30. Analytical Purity Determinations of Universal Food-Spice ... [https://pubmed.ncbi.nlm.nih.gov/36900526/]
• 31. Quality by design approach for development and validation of ... [https://bmcchem.biomedcentral.com/articles/10.1186/s13065-022-00864-4]
• 32. Assessment of Fractional Factorial Design for the Selection ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6912180/]
• 33. Validated Stability-Indicating RP-HPLC Method for ... [https://pubmed.ncbi.nlm.nih.gov/26997479/]
• 34. Hydro-organic mobile phase and factorial design application ... [https://bmcchem.biomedcentral.com/articles/10.1186/s13065-023-00990-7]
• 35. A comprehensive review of aflatoxin detection methods [https://www.sciencedirect.com/science/article/abs/pii/S0026265X25019290]
• 36. Current Developments of Analytical Methodologies for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9914313/]
• 37. Molecular and HPLC-based approaches for detection of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7956122/]
• 38. Detection of Aflatoxins in Different Matrices and Food- ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2020.01916/full]
• 39. What Is HPLC Method Robustness Assessment and Its ... [https://altabrisagroup.com/hplc-method-robustness-assessment/]
• 40. Determination of aflatoxins in eggs, milk, meat and ... [https://www.sciencedirect.com/science/article/abs/pii/S030881460801296X]
• 41. The Validation of a Simple, Robust, Stability-Indicating RP ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8837449/]
• 42. A robust method for quantifying 42 phenolic compounds by ... [https://www.sciencedirect.com/science/article/abs/pii/S0308814624024579]
• 43. Development and Validation of an HPLC-PDA Method for ... [https://www.mdpi.com/2076-3417/15/19/10797]
• 44. Regulation of and challenges in targeting NAD+ metabolism [https://pmc.ncbi.nlm.nih.gov/articles/PMC12456757/]
• 45. Absolute quantification of nicotinamide mononucleotide in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10762063/]
• 46. System suitability Requirements for a USP HPLC Method [https://www.mtc-usa.com/kb-article/aa-03965]
• 47. Validation of Stability-Indicating HPLC Methods for ... [https://www.chromatographyonline.com/view/validation-of-stability-indicating-hplc-methods-for-pharmaceuticals-overview-methodologies-and-case-studies]
• 48. Common Mistakes in Method Validation and How to Avoid ... [https://www.linkedin.com/pulse/common-mistakes-method-validation-how-avoid-them-part-oona-mcpolin-1]
• 49. Challenges in Analytical Method Development and ... [https://www.biopharminternational.com/view/challenges-analytical-method-development-and-validation]
• 50. Matrix Effects on Quantitation in Liquid Chromatography [https://www.chromatographyonline.com/view/matrix-effects-on-quantitation-in-liquid-chromatography-sources-and-solutions]
• 51. Compensate for or Minimize Matrix Effects? Strategies ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7412464/]
• 52. Modern Sample Preparation Methods for Food and ... [https://www.chromatographyonline.com/view/modern-sample-preparation-methods-food-and-environmental-laboratories]
• 53. HPLC Sample Preparation [https://www.organomation.com/hplc-sample-preparation]
• 54. Eco-Friendly AQbD-Driven HPLC: A Sustainable Approach to ... [https://jyoungpharm.org/10.5530/jyp.20250087]
• 55. Innovative sample preparation techniques in food analysis [https://www.sciencedirect.com/science/article/pii/S2772582025000440]
• 56. Green Approaches in High-Performance Liquid ... [https://www.mdpi.com/1420-3049/30/17/3573]
• 57. Testing the Robustness of Validated Methods for ... [https://link.springer.com/article/10.1007/s12161-012-9445-z]
• 58. Accelerated Stability Testing in Food Supplements ... [https://www.mdpi.com/2073-8994/16/4/493]
• 59. Effect of temperature and relative humidity on the stability ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8021666/]
• 60. The Role of HPLC Columns in Shaping a Greener ... [https://www.chromatographyonline.com/view/the-role-of-hplc-columns-in-shaping-a-greener-analytical-future]
• 61. Method Development and Optimization Software [https://www.acdlabs.com/solutions/developing-and-optimizing-lc-and-gc-methods/]
• 62. Greenness and performance evaluation of AI-predicted ... [https://www.sciencedirect.com/science/article/pii/S266683192500075X]
• 63. The Future of AI/ML in Liquid Chromatography [https://www.technologynetworks.com/analysis/articles/the-future-of-aiml-in-liquid-chromatography-407451]
• 64. HPLC Method Development Steps [https://www.thermofisher.com/us/en/home/industrial/chromatography/chromatography-learning-center/liquid-chromatography-information/hplc-method-development-steps.html]
• 65. Automation and Efficiency Improvement Solutions (HPLC ... [https://www.shimadzu.com/an/topics/campaign/automation-efficiency_improvement_solutions/12/index.html]
• 66. HPLC Column Performance Monitoring Documentation [https://altabrisagroup.com/hplc-column-performance-monitoring-documentation/]
• 67. 2025 Annual Meeting Scientific Sessions [https://www.aoac.org/2025-annual-meeting-exposition/2025-annual-meeting-scientific-sessions/]
• 68. Testing Method Performance [https://www.chromatographyonline.com/view/testing-method-performance]
• 69. Validation of the HPLC Analytical Method for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10171759/]
• 70. HPLC Testing and Analysis - Detailed Guide for Accurate [https://www.torontech.com/articles/hplc-testing-and-analysis-guide/]
• 71. Development and validation of an HPLC method for ... [https://www.sciencedirect.com/science/article/pii/S1872204025000404]
• 72. Performance and robustness analysis reveals phenotypic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10618107/]
• 73. Important Aspects of the Design of Experiments and Data ... [https://www.mdpi.com/1420-3049/29/24/6057]
• 74. Design and validation of a robust stability-indicating reversed ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12619242/]
• 75. Leveraging Multidimensional Modeling to Resolve ... [https://www.chromatographyonline.com/view/leveraging-multidimensional-modeling-to-resolve-frequent-separation-challenges-in-hplc]
• 76. Analytical quality by design aided RP-HPLC method for the ... [https://www.sciencedirect.com/science/article/pii/S2772577424000922]