Food Texture and Mouthfeel: A Scientific Guide for Biomedical and Clinical Applications

Abstract

This article provides a comprehensive scientific overview of food texture and mouthfeel, crucial sensory properties with significant implications for product development and clinical nutrition. Tailored for researchers and drug development professionals, it explores the physiological foundations of texture perception, details standardized instrumental and sensory evaluation methods, and addresses common formulation challenges. The content further covers validation techniques and emerging technologies like machine learning, synthesizing key takeaways to inform the development of specialized foods for dysphagia management, geriatric nutrition, and drug delivery systems.

The Science of Sensation: Defining Texture and Mouthfeel

Texture and mouthfeel represent distinct yet interconnected concepts fundamental to the sensory evaluation of food and pharmaceuticals. Texture encompasses the mechanical, geometrical, and surface properties of a product perceived largely through touch, including hardness, cohesiveness, and viscosity. Mouthfeel, in contrast, is the complex, integrated sensory experience resulting from the physical and chemical interactions between a substance and the oral cavity during consumption, encompassing tactile, thermal, and even mild pain stimuli. This whitepaper delineates the scientific demarcation between these two phenomena, detailing the instrumental and sensory methodologies for their evaluation, the physiological mechanisms underpinning their perception, and the experimental protocols essential for their rigorous investigation. Framed within the context of advancing food and oral drug formulation research, this guide provides researchers and product developers with a comprehensive framework for objectively quantifying these critical sensory attributes.

Flavor perception is a multisensory experience integrating taste, smell, and chemesthesis [1]. While taste and aroma have been extensively studied, mouthfeel remains less understood despite its significant role in overall sensory perception and consumer acceptance [1]. The terms "texture" and "mouthfeel" are often used interchangeably, but they refer to different concepts within sensory science.

• Texture is defined as "all the mechanical, geometrical and surface attributes of a product perceptible utilizing mechanical, tactile and, where appropriate, visual and auditory receptors" [2]. It describes the intrinsic, physical properties of a food or beverage, such as its firmness, crunchiness, or viscosity, which can often be measured instrumentally [1].
• Mouthfeel is a complex sensory perception that involves the complete physical, tactile, and textural experience in the mouth during consumption [1]. It is a subjective, perceptual response triggered by a product's texture, viscosity, and other mechanical and chemical properties, detected by chemosensory and somatosensory receptors in the oral cavity and primarily transmitted by the trigeminal nerve [1] [3].

The relationship is hierarchical: a product's measurable textural properties give rise to the perceptual experience of mouthfeel. This distinction is critical for researchers aiming to correlate objective instrumental measurements with subjective human perception.

Physiological Foundations of Perception

The perception of mouthfeel is a dynamic process involving multiple sensory systems and physiological mechanisms.

• The Role of the Trigeminal Nerve: Unlike taste (gustatory system) and smell (olfactory system), mouthfeel sensations are largely mediated by the trigeminal system [1]. This cranial nerve is responsible for detecting tactile sensations (pressure, vibration), temperature (cooling, warming), and chemesthetic sensations (astringency, pungency, burning) in the oral cavity [1] [3].
• Oral Processing: The perception of texture and mouthfeel evolves throughout the process of mastication. From first bite, through chewing, to swallowing, food is broken down, mixed with saliva, and formed into a bolus. Each stage releases different chemical components and alters the physical structure of the food, providing changing sensory input [1] [4].
• The Critical Function of Saliva: Saliva acts as a solvent and a lubricant. Its composition and flow rate significantly influence the perception of attributes like smoothness, astringency, and mouth-coating [1]. Astringency, for example, is perceived when salivary proteins are precipitated or aggregated by compounds like polyphenols, reducing oral lubrication and increasing friction [1] [2].

The integration of signals from these physiological pathways is essential to the overall flavor perception and underpins the complex experience of mouthfeel [1].

Quantitative Classification and Measurement

A scientific approach to texture and mouthfeel requires robust classification systems and measurement techniques.

Classification Frameworks

The Szczesniak classification system, developed at the General Foods Corporation, forms the historical basis for most modern texture analysis [4]. It categorizes textural characteristics into three primary groups, as detailed in Table 1.

Table 1: Szczesniak's Classification of Food Texture Characteristics [2] [4]

Category	Sub-category	Parameters	Description
Mechanical Properties	Primary	Hardness, Cohesiveness, Viscosity, Elasticity, Adhesiveness	Fundamental physical properties related to a product's response to stress.
	Secondary	Brittleness, Chewiness, Gumminess	Derived parameters calculated from combinations of primary properties.
Geometrical Properties	Particle Size & Shape	Grittiness, Graininess, Crumbliness	Attributes related to the size, shape, and arrangement of particles within the product.
	Particle Shape & Orientation	Fibrous, Flaky, Puffy	Attributes related to the orientation and form of structural components.
Other Properties	Moisture & Fat Content	Moistness, Oiliness, Greasiness	Surface properties influenced by the content and release of moisture and fat.

Instrumental Measurement Techniques

Instrumental methods provide objective, quantifiable data on physical properties. These are broadly categorized as fundamental, empirical, or imitative [2].

Table 2: Instrumental Methods for Texture and Mouthfeel Analysis [2]

Method Category	Technique	Measured Parameters	Typical Applications
Fundamental	Rheology	Viscosity, Viscoelasticity (G', G")	quantifying thickness of beverages, sauces.
	Tribology	Friction coefficient, Lubrication	Predicting smoothness, creaminess, astringency.
Empirical	Texture Profile Analysis (TPA)	Hardness, Springiness, Cohesiveness, Chewiness	Classifying mechanical properties of solid/semi-solid foods.
	Penetration/Puncture Tests	Breaking Force, Gel Strength	Evaluating firmness of gels (e.g., puddings, jellies).
Imitative	Multiple Extraction Cell	Chewiness, Toughness	Mimicking chewing action for meat and fibrous products.

Texture Profile Analysis (TPA), a seminal imitative technique, uses a texture analyzer to simulate two bites of a food sample. The resulting force-time curve yields quantitative data on multiple parameters like hardness, springiness, and cohesiveness in a single test [4].

Experimental Protocols for Sensory-Instrumental Correlation

Bridging the gap between instrumental data and human perception is a central challenge. The following integrated protocol outlines a standardized approach.

Integrated Sensory-Instrumental Workflow

This workflow, visualized in the diagram below, ensures that instrumental measurements closely mirror human sensory evaluation.

Diagram 1: Sensory-Instrumental Correlation Workflow

Case Study: Texture Optimization of a Milk Pudding Formulation

A recent study on milk pudding provides a clear example of this protocol in action, investigating the effects of hydrocolloids, starch, and sucrose on texture, syneresis, and sensory acceptability [5].

1. Objective: To determine the optimal formulation of milk pudding that minimizes syneresis (water separation) while maximizing sensory acceptability, by varying hydrocolloid type, modified starch concentration, and sucrose level [5].

2. Materials & Reagent Solutions: Table 3: Key Research Reagents for Milk Pudding Formulation Study [5]

Reagent	Function in the Formulation	Specific Example (from study)
Hydrocolloids	Gelling agent providing structure, mouthfeel, and water-holding capacity.	κ-carrageenan (brittle, firm gels), ι-carrageenan (elastic, soft gels), Gellan Gum, Agar, Gelatin.
Modified Starch	Thickening and stabilizing agent; influences gel strength and syneresis.	Modified waxy corn starch (CLEARAM CH 20).
Sucrose	Sweetener and texturizer; impacts sweetness perception and gel matrix properties.	Commercial food-grade sucrose.
Whole Milk	Base medium providing protein, fat, and water; interacts with hydrocolloids.	Standard whole milk.

3. Detailed Experimental Protocol:

• Sample Preparation: The pudding was prepared with 0.3% (w/w) of one of five polysaccharides (κ-carrageenan, ι-carrageenan, gellan gum, gelatin, or agar), 1-5% (w/w) modified waxy corn starch, and 2.5-7.5% (w/w) sucrose, with whole milk making up the remainder. The mixture was homogenized, sterilized (100°C for 20 min), dispensed into containers, and stored at 4°C for analysis at 7 and 14 days [5].
• Instrumental Texture Analysis: A Texture Analyzer (e.g., Stable Micro Systems TA-XT2) was used with a 5 mm spherical plunger for a penetration test. The following parameters were calculated from the resulting force-distance curve [5]:
  • Breaking Force (B.F.): The maximum force (g) required to break the gel.
  • Breaking Point (B.P.): The distance (mm) the plunger travels before the gel breaks.
  • Gel Strength (G.S.): Calculated as B.F. × B.P. (g×mm).
  • Rigidity (R.): Calculated as B.F. / B.P. (g/mm).
• Syneresis Measurement: Samples were weighed (w1) before storage. After 7 and 14 days at 4°C, expelled liquid was decanted and the remaining sample was weighed (w2). Syneresis was calculated as: [(w1 - w2) / w1] × 100 [5].
• Sensory Evaluation: A trained panel of 30 participants evaluated the samples for attributes including color, flavor, sweetness, springiness, and overall acceptability using a 9-point hedonic scale [5].

4. Key Findings and Correlation:

• Hydrocolloid Impact: Agar and κ-carrageenan produced the highest gel rigidity and strength, while gellan gum and ι-carrageenan were most effective at preventing syneresis [5].
• Starch Concentration: Contrary to some expectations, increasing modified corn starch concentration (1 to 5%) led to a decrease in gel strength, breaking force, and rigidity, and increased syneresis [5].
• Sucrolevel: Sucrose significantly increased gel strength and breaking force in puddings containing κ-carrageenan, gellan gum, and agar [5].
• Sensory-Instrumental Link: The study successfully identified a formulation optimum. The pudding with 5% sucrose, 0.3% gellan gum, and 1% modified waxy corn starch was found to have the highest overall acceptability, linking desirable sensory properties with specific instrumental measures (low syneresis, moderate gel strength) [5].

The Researcher's Toolkit: Emerging Trends and Future Perspectives

The field of texture and mouthfeel research is evolving rapidly, driven by technological advancement.

• Advanced Instrumental Techniques: The integration of rheology and tribology is becoming standard for understanding thickness and lubricity, respectively [2]. Furthermore, micro-analytical, tactile sensing, and biomimetic techniques are emerging as powerful tools to better mimic the complex oral environment [2].
• The Rise of AI and Machine Learning: Advanced statistical methods and AI are being leveraged to handle the complex, multi-dimensional data from sensory and instrumental studies, improving the predictive power for attributes like graininess and creaminess [2] [6].
• Market and Regulatory Drivers: The global market for food texture analyzers and texturizing agents is experiencing steady growth, projected to reach approximately $17.7 billion by 2033 [6] [7]. This is fueled by consumer demand for novel textures, clean-label products, and stringent quality control requirements in the food and pharmaceutical industries [6] [7].

The precise distinction between texture (the objective, physical property) and mouthfeel (the integrated, subjective perception) is foundational for scientific and product development progress. Through the application of structured classification systems like Szczesniak's, sophisticated instrumental techniques spanning rheology and tribology, and rigorous sensory-instrumental correlation protocols, researchers can deconstruct and master these complex sensory phenomena. As the field moves toward more biomimetic instruments and data-driven modeling, the ability to design tailored textural experiences and predictable mouthfeels—whether for a more satisfying food product or a more palatable oral drug—will become increasingly precise and achievable.

The perception of food is a multisensory experience where flavor is constructed from the integration of taste, smell, and, critically, mouthfeel. While taste and smell have been extensively studied, the role of the trigeminal nerve in mediating the physical and chemical sensations of food in the oral cavity is a burgeoning area of research. The trigeminal system is responsible for detecting textural, thermal, and irritant properties of food and beverages, such as creaminess, carbonation, spiciness, and cooling [1]. This whitepaper details the anatomy and physiology of the trigeminal sensory system, its central role in oral processing and mouthfeel perception, and provides a framework of advanced methodological approaches for researchers investigating this complex sensory pathway.

Neuroanatomy of the Trigeminal Sensory System

The trigeminal nerve (Cranial Nerve V) is the largest cranial nerve and is a mixed nerve, containing both sensory and motor fibers. Its extensive sensory components are primarily responsible for somatosensation from the orofacial region [8].

Peripheral Structures and Innervation

The sensory pathway begins with specialized receptors in the oral mucosa, teeth, and periodontum. Pseudounipolar neuron somas reside in the trigeminal (Gasserian) ganglion, located in the middle cranial fossa [9] [8]. These neurons project peripherally to the orofacial tissues and centrally to the brainstem.

• Peripheral Branching: The trigeminal nerve divides into three major branches distal to the ganglion [9] [8]:

  • Ophthalmic (V1): Provides sensory innervation to the forehead, upper eyelid, and cornea.
  • Maxillary (V2): Innervates the cheek, upper lip, upper jaw and teeth, and parts of the nasal cavity.
  • Mandibular (V3): The only branch with both sensory and motor components. It innervates the lower jaw and teeth, lower lip, and the anterior two-thirds of the tongue (somatic sensation, not taste) [8]. Its motor fibers control the muscles of mastication.

• Autonomic Integration: Fibers from the autonomic nervous system often join the trigeminal nerve branches to reach target tissues like salivary, lacrimal, and sweat glands, modulating the oral environment during food processing [9].

Central Processing Nuclei

Sensory information from the face and oral cavity is relayed to a complex of nuclei in the brainstem, collectively known as the trigeminal sensory nuclei [9]. Each subnucleus is specialized for processing different sensory modalities:

• Mesencephalic Nucleus (Me5): Uniquely contains the cell bodies of primary afferent neurons (rather than second-order neurons), which primarily convey proprioceptive information from masticatory muscles, the temporomandibular joint, and periodontal ligaments [9]. This nucleus is crucial for coordinating jaw movements during chewing.

• Principal Sensory Nucleus (Pr5): Located in the pons, this nucleus is the main relay for discriminative touch and pressure sensations from the orofacial region. Its neurons have low thresholds and small receptive fields, making it analogous to the dorsal column nuclei for the body [9].

• Spinal Trigeminal Nucleus (Sp5): This nucleus extends caudally from the pons through the medulla and into the upper cervical spinal cord. It is further subdivided and is primarily responsible for processing pain and temperature sensations [9] [8]. The functional convergence between the spinal trigeminal nucleus and the upper cervical spinal cord forms a trigeminocervical complex (TCC), which allows for the referral of pain between the neck and face, a phenomenon relevant to certain headache disorders [8].

Table 1: Major Trigeminal Sensory Nuclei and Their Functions

Nucleus	Location	Primary Sensory Modalities	Key Projections
Mesencephalic Nucleus (Me5)	Midbrain to Pons	Proprioception (jaw, teeth)	Trigeminal Motor Nucleus, Pr5, Sp5, Supratrigeminal Region
Principal Sensory Nucleus (Pr5)	Pons	Discriminative Touch, Pressure	Contralateral Thalamus (VPM)
Spinal Trigeminal Nucleus (Sp5)	Pons to Cervical Spinal Cord	Pain, Temperature	Contralateral Thalamus, Brainstem Parasympathetic Nuclei

Higher-order processing occurs when information from these nuclei is relayed via the thalamus to the primary somatosensory cortex and the insular cortex, where conscious perception of oral texture and irritation occurs [9]. Integration with olfactory and taste information occurs in the orbitofrontal cortex (OFC), creating the unified percept of flavor [10].

Figure 1: Central Trigeminal Sensory Pathway from Orofacial Stimuli to Cortical Perception.

The Trigeminal Nerve in Oral Processing and Mouthfeel

Mouthfeel is a complex, multidimensional perception involving the physical, tactile, and textural sensations experienced in the mouth when consuming food or beverages [1]. The trigeminal nerve is the primary conduit for these sensations, which are distinct from taste (sweet, salty, sour, bitter, umami) mediated by the facial, glossopharyngeal, and vagus nerves [10].

Key Trigeminal Sensations in Flavor

• Thermal Sensations: Compounds like menthol (cooling) and capsaicin (warming) activate specific thermoreceptors (e.g., TRPM8 and TRPV1 channels) on trigeminal afferents, creating sensations of cold or heat independent of actual temperature [9] [11].
• Chemical Irritation/Pungency: Capsaicin from chili peppers, piperine from black pepper, and allyl isothiocyanate from mustard and wasabi activate nociceptive (pain) pathways, perceived as pungency, burning, or tingling [1] [11].
• Astringency: This sensation of dryness and puckering, often from polyphenols in tea, red wine, or unripe fruit, is thought to involve trigeminal mechanoreceptors responding to increased friction from lubricity loss as salivary proteins precipitate [1].
• Carbonation: The "fizz" from carbonated beverages is primarily a trigeminal sensation, likely mediated by the conversion of CO2 to carbonic acid, which stimulates nociceptors [1].
• Texture and Viscosity: Mechanoreceptors innervated by the trigeminal nerve detect physical properties like thickness, creaminess, grittiness, and smoothness, critical for the perception of quality and acceptability [1] [12].

Advanced Methodologies for Investigating Trigeminal Perception

Research into trigeminal-mediated mouthfeel requires a combination of sophisticated sensory evaluation techniques and cutting-edge instrumental analysis.

Sensory Evaluation Protocols

Human sensory testing remains the gold standard for linking physicochemical properties to perceptual experience.

• Descriptive Analysis: Using trained expert panels to quantitatively profile the intensity of specific sensory attributes (e.g., creaminess, astringency, burning) using structured scales [1]. This provides a comprehensive sensory map of a product.
• Temporal Methods: Techniques such as Temporal Dominance of Sensations (TDS) track how sensory perceptions (e.g., initial coolness, lingering heat) evolve over time during consumption, providing a dynamic view of the sensory experience that single-point ratings miss [1] [13].
• Just-About-Right (JAR) Scaling and Penalty Analysis: Consumers rate whether an attribute (e.g., thickness) is "too weak," "just-about-right," or "too strong." Penalty analysis then quantifies how much overall liking drops when an attribute is not at its ideal level, identifying critical formulation targets and "sensory danger zones" [13].

Quantitative Data Analytics and Machine Learning

Advanced analytics are essential to decode the complex, multivariate data generated from sensory studies.

• Multivariate Analysis: Techniques like Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) identify which combinations of sensory attributes drive consumer preference and map products in a multidimensional sensory space to reveal market opportunities [13].
• Cluster Analysis: Identifies distinct consumer segments with fundamentally different sensory preferences, preventing the dilution of product design by targeting the "average" consumer [13].
• AI and Machine Learning: Recent research uses neural networks to predict perceived texture based on a food's physical properties. For instance, a sensory-based autoencoder trained on limited data can accurately predict human texture perception, accelerating product development by reducing reliance on iterative human testing [12].

Table 2: Key Analytical Methods for Sensory Data Interpretation

Method	Primary Function	Application Example
Principal Component Analysis (PCA)	Reduces data dimensionality to reveal hidden underlying factors driving perception.	Identifying that 83% of skincare preference is driven by three previously ignored sensory dimensions [13].
Partial Least Squares Regression (PLS)	Models relationships between independent variables (X) and dependent variables (Y).	Linking specific chemical compounds in beer to perceived mouthfeel attributes like smoothness and astringency [1] [13].
Penalty Analysis	Quantifies the drop in overall liking when a sensory attribute deviates from its "ideal" intensity.	Determining that a 10% deviation from ideal viscosity results in a 15% drop in overall liking for a soup product.
Sensory-Based Autoencoder (AI)	Learns a compressed representation of human texture perception from limited physical data.	Predicting the perceived thickness of bouillon samples based on lab-measured rheological properties [12].

Instrumental and Physiological Measures

• Rheology and Tribology: Rheology measures bulk material properties (e.g., viscosity), while tribology studies friction and lubrication between surfaces, mimicking the tongue-palate interaction. Together, they provide physical proxies for sensations like thickness and smoothness [1].
• Electrophysiology: In preclinical models, single-unit recordings can measure action potentials from trigeminal ganglion neurons or brainstem nuclei in response to specific chemical or mechanical stimuli, directly linking stimulus to neural response [9].
• Neuroimaging: Functional MRI (fMRI) can localize brain activity in regions like the insula and OFC during trigeminal stimulation, elucidating the central processing of mouthfeel [14].

The Scientist's Toolkit: Essential Reagents and Materials

Table 3: Key Research Reagents and Tools for Trigeminal and Mouthfeel Research

Item	Function/Description	Example Application
Reference Trigeminal Compounds	Pure chemical agonists/antagonists for specific receptors.	Menthol (TRPM8 agonist, cooling), Capsaicin (TRPV1 agonist, heat), Hydroxy-α-sanshool (tingling) [11].
Electronic Tongue	Array of chemical sensors to simulate basic taste perception.	Preliminary screening of taste-trigeminal interactions in solution [1].
Rheometer	Instrument to measure viscosity and viscoelastic properties of fluids.	Quantifying the shear-thinning behavior of ketchup or yogurt, correlating to perceived thickness [1] [12].
Tribometer	Instrument to measure friction and lubricity between surfaces.	Simulating oral conditions to predict sensory attributes like smoothness, creaminess, and astringency [1].
Gustometer	Device for controlled delivery of liquid tastants/irritants.	Psychophysical testing of taste and trigeminal detection thresholds [10].
QCM (Quartz Crystal Microbalance) Sensors	Measures mass and viscoelastic changes on a sensor surface.	Studying the adsorption of astringent compounds (e.g., polyphenols) onto protein-coated surfaces [1].

Figure 2: Integrated Experimental Workflow for Mouthfeel Optimization.

The trigeminal nerve is a fundamental component of flavor perception, responsible for translating the physical and chemical properties of food into the rich sensory experience known as mouthfeel. A deep understanding of its anatomy—from peripheral receptors to central processing nuclei—is essential for researchers aiming to modulate food perception. The convergence of advanced sensory methodologies, powerful quantitative analytics, and emerging AI tools provides an unprecedented ability to decode this complex relationship. This knowledge is pivotal for addressing key industry challenges, such as developing successful low-fat, low-sugar, and plant-based products, where achieving the correct mouthfeel is often the primary barrier to consumer acceptance. Future research will continue to bridge the gap between measurable physical properties, underlying neurophysiology, and conscious perceptual experience, driving innovation in food science and related fields.

Food texture is a fundamental sensory property that, along with taste and smell, determines the overall flavor and acceptability of food and beverage products [1]. For researchers and product development professionals, texture is defined as the "sensory and functional manifestation of the structural, mechanical and surface properties of foods detected through the senses of vision, hearing, touch and kinesthetics" [15] [16]. The related term mouthfeel refers more specifically to "all of the tactile (feel) properties perceived from the time at which solid, semi-solid or liquid foods or beverages are placed in the mouth until they are swallowed" [15]. This includes sensations experienced during the entire oral processing sequence—from initial placement in the mouth through mastication and finally swallowing [17] [4]. In pharmaceutical development, these same principles are crucial for patient compliance and acceptability of medical nutrition products and oral dosage forms [15].

Texture perception originates from a food's physical properties, which are traditionally categorized into three primary groups: mechanical, geometrical, and moisture properties [16] [4]. Mechanical properties relate to a substance's response to stress, geometrical properties concern particulate structure and orientation, and moisture properties involve water and fat interactions [18] [16]. Understanding this terminology provides researchers with a standardized vocabulary for describing, measuring, and optimizing the sensory attributes that significantly influence consumer preference and product success [1] [16].

Mechanical Properties

Mechanical properties describe a food's response to applied forces and deformations, representing its fundamental rheological characteristics [4]. These properties are typically perceived through the sense of touch and physical interaction during chewing and manipulation in the mouth [16].

Table 1: Primary Mechanical Properties

Term	Definition	Sensory Evaluation Technique	Research Significance
Hardness	The force required to deform a food to a given distance or the peak force from compression [18] [17].	Place sample between molar teeth and bite down evenly, evaluating force required to compress [4].	One of the eight fundamental texture parameters; key quality indicator for products like pickles and cheeses [18] [4].
Cohesiveness	The degree to which a food sticks to itself or deforms before rupturing [18] [17].	Place sample between molar teeth; compress and evaluate deformation before rupture [4].	Primary parameter in Texture Profile Analysis (TPA); influences chewiness and gumminess [18].
Adhesiveness	The work necessary to overcome the attractive forces between a food and other surfaces (e.g., mouth, teeth) [18].	Place sample on tongue, press against palate, evaluate force required to remove with tongue [4].	Related to stickiness; crucial for swallowing efficiency and patient safety in dysphagia foods [18] [15].
Viscosity	The resistance to flow or force required to draw a liquid over the tongue [18] [17].	Draw liquid from a spoon over the tongue by slurping, evaluating force required [4].	Fundamental parameter for fluids; heavily influences satiety and swallowing safety [17] [15].
Springiness	The ability of a food to return to its original shape after partial compression [18].	Compress sample partially between molars or between tongue and palate; remove force and evaluate recovery [4].	Originally termed "elasticity"; measured as deformation ratio in TPA [18].

Table 2: Secondary and Quantitative Mechanical Properties

Term	Definition	Calculation/Measurement	Research Significance
Fracturability	The force with which a food crumbles, cracks, or shatters [18] [17].	Force with which the food moves away from teeth during compression [4].	Replaces "brittleness"; mutually exclusive with chewiness and gumminess [18].
Chewiness	The energy required to masticate a solid food into a state ready for swallowing [18] [17].	Hardness × Cohesiveness × Springiness [18].	Secondary TPA parameter; applies only to solid, deformable foods [18].
Gumminess	The energy required to disintegrate a semi-solid food to a state ready for swallowing [18] [17].	Hardness × Cohesiveness [18].	Secondary TPA parameter; applies only to semi-solid foods [18].
Springiness Index	A dimensionless ratio representing a food's recovery from deformation [18].	Deformation of 2nd compression ÷ Deformation of 1st compression (TPA) [18].	Allows comparison between products by removing sample size/shape inconsistencies [18].

Geometrical Properties

Geometrical properties are perceived characteristics related to the size, shape, orientation, and physical arrangement of particles or structural components within a food product [16]. These attributes are derived from the macro- and microstructure of the food and are detected through tactile sensations and sometimes auditory cues [16].

Table 3: Geometrical Properties

Term	Definition	Sensory Experience	Research & Quality Control Applications
Graininess	The presence of small, detectable grainy particles [17].	Perception of small, grainy particles in the mouth.	Undesirable in products like smooth chocolate or yogurt; indicates incomplete processing or crystallization [1].
Grittiness	The presence of small, hard particles [18].	Sensation of small, hard, abrasive particles.	Defect in products like sauces or puddings; often caused by precipitate formation or incomplete hydration.
Flakiness	A structure of readily separated layers [18].	Perception of layers separating during chewing.	Critical quality attribute for pastries and pies; indicates proper lamination and fat distribution.
Fibrousness	The presence of readily separated thread-like structures [18] [17].	Perception of thread-like structures requiring separation.	Key attribute for meat and vegetable products; influences perceived tenderness and chewing pattern.
Cellular Aerated	A predominantly regular, void structure [18].	Perception of a uniform, air-filled structure.	Characteristic of breads, sponges, and foams; correlates with specific volume and crumb structure.
Smoothness	The absence of any detectable solid particles, lumps, or bumps [18] [17].	Perception of a uniform, homogenous texture without particulates.	Desirable in products like yogurt, ice cream, and sauces; indicates effective homogenization and stabilization [1].

Moisture and Fat-Related Properties

Moisture and fat-related properties encompass sensations related to the presence, release, and absorption of water and lipids in the oral cavity during food consumption [16]. These properties significantly influence mouthfeel and are critical for product acceptability, particularly for medical nutrition products where they impact swallowing safety and efficiency [15].

Table 4: Moisture and Fat-Related Properties

Term	Definition	Sensory Experience	Clinical & Product Development Significance
Moistness	The perception of a moderate amount of free fluids, neither increasing nor decreasing oral moisture [18].	Sensation of balanced moisture content without significant fluid release.	Optimal for many solid foods; indicates proper water activity and formulation balance.
Wetness	An immediate increase in free fluids in the mouth upon ingestion [18].	Immediate sensation of increased moisture.	Characteristic of high-moisture foods like fruits; influences freshness perception.
Juiciness	A progressive increase in free fluids during mastication [18].	Progressive release of fluids during chewing.	Critical quality attribute for meats and fruits; correlates with consumer satisfaction and perceived quality.
Dryness	A reduction or absence of free fluids in the mouth; the degree to which a sample feels dry [18] [17].	Sensation of moisture absorption by the food.	Undesirable in many products but characteristic of crackers and biscuits; can indicate staleness in baked goods.
Oiliness	The sensation resulting from the presence of thick, oily liquid in the mouth [18] [19].	Perception of a thick, oily coating.	Characteristic of fried foods and some dressings; contributes to richness and flavor release.
Greasiness	The sensation of lubricity and oil-film deposition associated with fat content [16].	Perception of a slippery, lubricating fat film.	Can be desirable (e.g., fried foods) or undesirable (e.g., excessive surface oil); influences flavor persistence.
Mouthcoating	The type and degree of coating remaining in the mouth after mastication and swallowing [17] [19].	Residual film adhering to oral surfaces after swallowing.	Important for flavor persistence; excessive coating can be undesirable and contribute to satiety [17].
Astringency	A complex of drying, roughing, and puckering sensations [19].	Drying, puckering, roughing sensation often described as "furry" or "dust-in-the-mouth".	Caused by polyphenol-protein interactions; particularly relevant to wine, tea, and some fruits [19].

Experimental Protocols for Texture Analysis

Texture Profile Analysis (TPA)

Texture Profile Analysis is a fundamental two-bite compression test that imitates the action of chewing, providing quantitative measurements of multiple textural parameters [18] [4].

Protocol:

• Sample Preparation: Prepare samples of uniform size and shape. For bulk products (e.g., peas, kibble), use a representative aggregate sample [18].
• Instrument Setup: Configure a texture analyzer with a compression platen or probe appropriate for the sample type. For most solid foods, a cylindrical probe with diameter larger than the sample is used.
• Test Parameters: Set compression to 25-75% of original sample height (strain). Use a constant crosshead speed (typically 1-2 mm/s). Include a 1-5 second pause between the two compression cycles to simulate chewing rhythm [18].
• Data Collection: Perform compression test while recording force-time data. The resulting curve provides measurements for hardness, cohesiveness, springiness, adhesiveness, chewiness, and gumminess [18] [4].
• Statistical Analysis: Conduct multiple replicates (typically n≥10) and report means with standard deviations.

Figure 1: Texture Profile Analysis (TPA) Experimental Workflow

Sensory Panel Training for Mouthfeel Assessment

Trained human panels remain the gold standard for correlating instrumental measurements with sensory perception [15] [4].

Protocol:

• Panel Selection: Screen participants for sensory acuity, availability, and motivation. Typically 8-12 panelists are selected.
• Terminology Development: Introduce standardized texture terminology using the established glossaries [18] [17]. Provide physical reference standards for each attribute.
• Attribute Intensity Scaling: Train panelists to use structured scales (e.g., 0-9 or 0-15 point scales) for rating attribute intensities. Include anchor points with reference products.
• Technique Standardization: Teach standardized evaluation techniques for each attribute, including proper placement in mouth, chewing methodology, and evaluation timing [4].
• Validation: Assess panel performance through reproducibility tests, discrimination tests, and agreement with instrumental measures.

Instrumental Methods and Research Toolkit

Instrumental methods provide objective, quantitative measurements of food texture properties that complement sensory evaluation [1] [16].

Table 5: Research Toolkit for Texture and Mouthfeel Analysis

Instrument/Technique	Measurement Principle	Applications	Correlated Sensory Attributes
Texture Analyzer	Measures force, distance, and time during mechanical deformation [18] [4].	TPA, puncture, compression, extrusion tests for solid and semi-solid foods.	Hardness, cohesiveness, springiness, fracturability, chewiness [18].
Rheometer	Applies controlled stress/strain to measure flow and deformation properties [1] [4].	Viscosity, viscoelasticity, yield stress of fluids and semi-solids.	Viscosity, thickness, spreadability, gumminess [4].
Tribometer	Measures friction and lubrication properties between surfaces [1].	Lubrication behavior, thin-film rheology in oral-mimicking conditions.	Smoothness, slipperiness, astringency, mouthcoating [1].
Saliva Composition Analysis	Quantifies salivary protein profile, mucin content, and enzymatic activity [19].	Understanding salivary interactions with food components.	Astringency, drying, mouthcoating perception [19].
Particle Size Analyzer	Determines size distribution of particles in suspensions and emulsions [20].	Quantifying geometrical properties in products like chocolate, sauces.	Grittiness, graininess, smoothness [20].

Figure 2: Instrumental Methods for Texture Property Analysis

Applications in Research and Product Development

The systematic classification of texture properties provides a critical framework for multiple research and development applications. In medical nutrition product development, specific mouthfeel attributes such as cohesiveness, adhesiveness, and viscosity are engineered to ensure swallowing safety and efficiency for patients with dysphagia or poor dental status [15]. These textural adaptations directly impact nutritional status by influencing product acceptance and intake compliance [15].

In food product development, texture modification serves as a key strategy for creating products that meet consumer expectations and regulatory requirements. The interaction between food components and oral processing physiology—particularly saliva composition and flow—creates complex mouthfeel sensations that can be optimized through understanding these fundamental properties [1] [19]. For pharmaceutical applications, controlling geometrical properties like particle size and shape can significantly influence drug dissolution rates and bioavailability, while moisture properties affect stability and shelf life [20].

The continued development of standardized terminology, such as the specialized mouthfeel wheels for specific product categories, enables clearer communication between sensory scientists, product developers, healthcare professionals, and consumers [15]. This shared vocabulary is essential for advancing research in food physics, oral processing, and sensory science, ultimately leading to products that better meet consumer needs and therapeutic requirements.

The Critical Role of Saliva in Modulating Mouthfeel and Flavor Release

Saliva is a complex biological fluid that actively governs the dynamic sensory experience of food consumption. This whitepaper examines the multifaceted role of saliva in determining mouthfeel and flavor release during oral processing. Through its composition and physicochemical properties, saliva influences critical processes including bolus formation, texture degradation, lubrication, and the release of volatile and non-volatile compounds. Understanding these mechanisms is essential for researchers and product development professionals working in food science, pharmaceuticals, and consumer health to design products with optimized sensory profiles and delivery systems.

Saliva is far more than a simple digestive fluid; it is a central mediator in the oral sensory experience. As a complex biofluid, saliva supports oral homeostasis through lubrication, antimicrobial activity, and tooth integrity maintenance while simultaneously functioning as a critical transport medium for flavor compounds and signaling metabolites [21]. The biochemical interplay between saliva and food components begins upon entry into the mouth and evolves throughout the mastication process, creating a temporally dynamic sensory perception that determines ultimate product acceptance [22].

For researchers investigating food texture and mouthfeel, understanding salivary function is paramount. The unique makeup of an individual's saliva—including its enzymatic profile, viscosity, and volume—can significantly alter how they perceive food texture and flavor [23]. Recent research demonstrates that variations in salivary enzymes directly impact food breakdown kinetics, while differences in emulsification capacity affect fat perception and overall mouthfeel [23]. This technical guide explores the mechanisms behind these processes and provides methodologies for standardized investigation within food texture research.

Saliva Composition and Functional Properties

Saliva's functionality in oral processing stems from its complex composition, which includes water, electrolytes, mucus, enzymes, and antimicrobial compounds. The primary components relevant to mouthfeel and flavor modulation include:

• α-Amylase: This enzyme catalyzes the hydrolysis of starch into smaller polysaccharides and maltose, directly impacting the viscosity and texture of starch-based foods [23].
• Lipase: Initiates lipid digestion, affecting creaminess and fat-related mouthfeel attributes.
• Mucins: Glycoproteins that provide viscosity and lubricating properties, crucial for bolus formation and swallowability.
• Proline-Rich Proteins: Involved in tannin binding, which influences astringency perception.
• Electrolytes: Buffer capacity and ionic composition affect flavor compound solubility and perception.

Salivary properties exhibit significant inter-individual variation, creating a personalized oral environment that shapes food perception. Factors such as flow rate, pH, and enzymatic activity vary across populations and are influenced by genetics, diet, health status, and environmental factors [21] [23].

Mechanisms of Mouthfeel Modulation

Texture Transformation During Mastication

The physical transformation of food during mastication is intimately connected with salivary secretion and mixing. As food is chewed, saliva facilitates breakdown through hydration and enzymatic action while simultaneously enabling bolus formation through its binding and lubricating properties. Research using robotic chewing systems has quantified how different food textures evolve throughout the oral processing period [24].

Table 1: Food Texture Changes During Robotic Mastication of Different Foods

Food Type	Texture Parameter	Change During Mastication	Primary Influencing Factor
Roasted Peanuts	Hardness	81.5% reduction	Chewing cycles
Roasted Peanuts	Springiness	Significant reduction	Saliva secretion rate
Roasted Peanuts	Adhesive Force	Increase then decrease	Chewing stages
White Bread	Hardness	Initial increase then decrease	Compression from chewing
White Bread	Adhesive Force	Progressive increase	Saliva secretion rate
White Bread	Cohesiveness	Progressive increase	Chewing trajectory

The data reveal distinct transformation patterns for different food matrices. For roasted peanuts, texture changes are predominantly governed by chewing stages, with hardness showing a dramatic 81.5% reduction over the chewing process [24]. In contrast, white bread exhibits more complex behavior, initially increasing in hardness due to compression before gradual softening, with adhesive properties chiefly influenced by saliva secretion rate [24].

Lubrication and Astringency Perception

Saliva's lubricating properties fundamentally influence mouthfeel attributes such as smoothness, creaminess, and astringency. The mucin glycoproteins in saliva form a protective layer over oral surfaces, reducing friction between food particles and oral mucosa. During fat consumption, salivary components contribute to emulsion formation, with individuals whose saliva produces stable emulsions demonstrating enhanced ability to taste fat [23].

Astringency arises when salivary proteins (particularly proline-rich proteins and histatins) interact with polyphenolic compounds (e.g., tannins in tea, wine, or certain fruits), causing protein precipitation and reduced lubrication. This perceived dryness or roughness represents a tactile phenomenon rather than a true taste, directly resulting from saliva's altered lubricating capacity.

Flavor Release Dynamics

Mechanisms of Flavor Compound Release

Saliva modulates flavor perception through multiple mechanisms affecting both aroma (volatile) and taste (non-volatile) compounds:

• Partitioning of Volatiles: Saliva influences the release of aroma compounds from the food matrix to the air phase (headspace) through dilution, binding, and enzymatic modification.
• Taste Compound Interaction: Salivary components can bind to or modify taste molecules, affecting their availability to taste receptors.
• Enzymatic Transformation: Salivary enzymes (e.g., amylase, lipase) break down macromolecules, releasing encapsulated flavors and changing matrix properties.

Research on soft-boiled chicken demonstrates the dynamic nature of flavor release during oral processing, where the addition of chicken skin altered lubrication properties and subsequently affected the perception of umami and sweet tastes, highlighting fat's critical role in flavor modulation [22].

Individual Variation in Flavor Perception

The unique biochemical composition of an individual's saliva creates personalized flavor perception profiles. Studies show that variations in salivary α-amylase activity directly impact the rate of starch breakdown in foods like pudding, significantly altering texture evolution and sweetness release kinetics [23]. Similarly, differences in saliva's emulsification capacity affect how individuals perceive fat-rich foods, with those whose saliva forms stable emulsions showing better fat taste sensitivity [23].

Methodological Approaches for Saliva Research

Standardized Saliva Collection and Processing

Rigorous methodological protocols are essential for reproducible saliva research. The following procedures ensure sample integrity and minimize pre-analytical variability:

Basic Protocol 1: Saliva Collection by Passive Drool Method

• Pre-collection Instructions: Participants should refrain from eating, drinking, toothbrushing, or using mouthwash for at least one hour before collection. Perform a water rinse immediately prior to sample collection.
• Collection Procedure: Participants allow saliva to pool in the mouth floor and passively drool through a straw into a pre-chilled collection tube.
• Timing Considerations: Collect at consistent times of day to control for diurnal variations. Document factors potentially influencing composition (medications, health status, stress levels).
• Initial Processing: Centrifuge samples at 2600 × g for 15 minutes at 4°C to separate clear supernatant from cellular debris [21].

Basic Protocol 2: Processing, Storage, and Characterization

• Aliquoting and Storage: Divide supernatant into small aliquots in cryovials. Store at -80°C for long-term preservation; avoid repeated freeze-thaw cycles.
• Flow Rate Measurement: Calculate as volume collected divided by collection time (mL/min).
• pH Assessment: Use pH indicator strips or microelectrode for precise measurement.
• Visual Assessment: Employ standardized scale to record discoloration (e.g., blood contamination) and viscosity [21].

In Vitro Mastication Models

Robotic chewing systems provide controlled, reproducible platforms for studying food breakdown and flavor release:

Robotic Chewing Protocol for Texture Analysis

• System Setup: Utilize a robotic chewing system capable of simulating human masticatory patterns with adjustable parameters including chewing trajectory, occlusal force, and artificial saliva flow rate [24].
• Parameter Selection: Program specific chewing behaviors by combining different trajectories (vertical, horizontal, circular), occlusal forces (light, medium, vigorous), and saliva secretion rates (low, medium, high).
• Sample Processing: Chew food samples for predetermined percentages (0%, 25%, 50%, 75%, 100%) of total chewing cycles needed for swallowing, as determined through in vivo studies.
• Texture Analysis: Submit chewed boluses to Texture Profile Analysis (TPA) to measure hardness, adhesiveness, cohesiveness, springiness, and resilience [24].
• Data Analysis: Employ Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) to correlate chewing behaviors with texture changes [24].

Table 2: Research Reagent Solutions for Saliva and Oral Processing Research

Reagent/Equipment	Function/Application	Specific Examples
Artificial Saliva (Biotene Dry Mouth Relief)	Standardized saliva substitute for in vitro mastication studies	Biotene Dry Mouth Relief Mouthwash used in robotic chewing studies [24]
Passive Drool Apparatus	Non-stimulated whole saliva collection	Sterile straws and pre-chilled polypropylene tubes [21]
Texture Profile Analyzer (TPA)	Quantification of mechanical properties of food boluses	Measurement of hardness, adhesiveness, cohesiveness, springiness, resilience [24]
Electronic Tongue	Taste perception analysis without human panel variability	Detection of umami, sweet, salty, sour, bitter compounds in food boluses [22]
Electronic Nose	Volatile compound detection and aroma profiling	Analysis of aromatic signals released during mastication [22]
Centrifuge	Clarification of saliva samples	Removal of cellular debris and mucins (2600 × g for 15 minutes) [21]

Visualization of Saliva-Food Interactions

The following diagrams illustrate key processes and relationships in saliva-mediated mouthfeel and flavor release.

Diagram 1: Saliva-Mediated Food Breakdown Pathway

Diagram 2: Experimental Workflow for Oral Processing Research

Implications for Product Development

Understanding saliva-food interactions has significant applications across multiple industries:

Food Design and Development

Insights into salivary modulation of texture and flavor enable targeted food design. Formulators can create products that leverage salivary interactions to achieve desired sensory profiles, such as optimizing starch-based foods for specific α-amylase activity levels or designing fat-reduced products that maintain satisfactory mouthfeel through enhanced lubrication properties [23].

Pharmaceutical Applications

In drug development, understanding oral processing informs the design of orally disintegrating tablets and buccal delivery systems. Knowledge of how saliva affects disintegration, drug release, and taste masking can improve patient compliance, particularly for pediatric and geriatric populations [25]. Research presented at the GLP1 & Oral Peptides conference highlights ongoing innovations in oral peptide delivery that must account for salivary variables [26].

Saliva serves as an active mediator rather than a passive spectator in the oral experience of food. Its composition and properties dynamically influence texture transformation, lubrication, and flavor release through complex biochemical and physical mechanisms. Standardized methodologies for saliva collection and in vitro mastication studies provide researchers with robust tools to investigate these processes systematically. As understanding of individual variations in salivary function grows, so does the potential for personalized product development in both food and pharmaceutical sectors. Future research integrating multi-omics approaches with sensory science will further elucidate the intricate relationships between salivary components and perceptual outcomes, driving innovation in product design and delivery systems.

Measuring the Immeasurable: Instrumental and Sensory Analysis Techniques

Texture is a fundamental sensory property that significantly influences consumer food preferences, eating experiences, and product acceptance in the market [1]. Instrumental Texture Profile Analysis (TPA) represents a critical methodological approach in food science and pharmaceutical development for quantifying the textural properties of materials in a manner that simulates human mastication. This technical guide explores the evolution, current methodologies, and applications of TPA with a specific focus on advanced technologies designed to mimic the human bite. The perception of mouthfeel encompasses complex physical, tactile, and textural sensations experienced during consumption, which, despite its significance, remains difficult to measure accurately with conventional instruments [1]. While traditional TPA provides valuable mechanical data, a significant gap often exists between these instrumental measurements and actual human sensory evaluation, driving innovation in biomimetic testing approaches that more faithfully replicate the complexity of human oral processing [27] [28].

The challenge in instrumental texture analysis lies in the sophisticated biomechanics of human mastication, which involves complex jaw movements, variable forces, and physiological factors such as dental morphology, saliva composition, and temporomandibular joint dynamics [1] [28]. This guide examines how emerging technologies—from biomimetic probes to artificial intelligence and humanoid chewing robots—are addressing these challenges to create more predictive relationships between instrumental measurements and human sensory perception, ultimately enabling more efficient product development across food and pharmaceutical industries.

Fundamentals of Human Oral Processing

Understanding the biomechanical and physiological aspects of human mastication is essential for developing effective instrumental texture simulation. Human chewing represents a complex process involving musculoskeletal components, neural control, and chemical environments that collectively contribute to texture perception.

Key Physiological Factors in Mastication

The effectiveness of mastication is governed by several physiological parameters that influence how food is broken down and perceived:

• Dental Morphology: The specific shape and arrangement of molars, including their cusps and fissures, determine the crushing, shearing, and grinding actions during chewing [28]. The occlusal surface geometry plays a particularly crucial role in food breakdown efficiency.

• Jaw Physiology: The Frankfort-mandibular plane angle (FMA) and Bennett angle (BA) are critical cephalometric measurements that influence jaw movement and biting forces [28]. The FMA indicates the vertical jaw relationship affecting bite force direction, while the BA relates to mandibular movement during lateral excursions, reflecting temporomandibular joint influence on chewing efficiency.

• Saliva Composition and Flow: Saliva acts as a medium for dissolving flavor compounds and modulating textural sensations through its enzymatic content and lubrication properties, significantly influencing perceptions of smoothness, astringency, and mouth-coating [1].

• Trigeminal Nerve Function: As the primary somatosensory pathway for the oral cavity, the trigeminal nerve transmits tactile, thermal, and chemical irritant signals that contribute substantially to mouthfeel perception [1].

During oral processing, food undergoes a progressive transformation from its initial state to a swallowable bolus, with continuous feedback between mechanical breakdown and sensory perception [1]. This complex process involves the integration of multiple sensory signals—including tactile, thermal, and chemical inputs—that collectively create the multidimensional experience of texture [1].

From Mechanical Properties to Sensory Perception

The relationship between a food's physical properties and its sensory perception involves complex transduction mechanisms:

• Structural Properties: Mechanical characteristics such as hardness, fracturability, and cohesiveness are detected by mechanoreceptors in the oral cavity and periodontal ligaments [1].

• Surface Properties: Attributes like smoothness, grittiness, and oiliness are perceived through tactile receptors in the mucosa and tongue [1].

• Acoustical Components: Sounds generated during chewing, particularly for crispy and crunchy foods, contribute significantly to texture perception through bone conduction and air transmission [1].

The integration of these signals occurs through central nervous system processing, where they are combined with taste and aroma cues to form the comprehensive sensory experience of mouthfeel [1]. This multidimensional nature of texture perception explains why simple mechanical measurements often fail to fully predict human sensory responses, necessitating more sophisticated biomimetic approaches.

Advanced Biomimetic Approaches in TPA

Biomimetic Molar Probe Design and Performance

Recent innovations in probe design have focused on creating instruments that more accurately replicate human dental anatomy and chewing dynamics. A 2025 case study with hazelnuts demonstrated the development of two biomimetic probes (M1 and M2) based on human molar morphology [27]. These probes were specifically engineered to replicate the crushing patterns of human mastication, with their performance compared against conventional texture analyzer probes (P/50 and HPD) across multiple test speeds (0.1, 1.0, and 10.0 mm/s) [27].

The experimental protocol involved several key methodological steps:

• Probe Fabrication: Creation of M1 and M2 probes using precise dental morphology data from human molars
• Compression Testing: Uniaxial compression of four hazelnut samples using both biomimetic and conventional probes
• Multi-modal Data Collection: Measurement of particle size distribution, surface electromyography (EMG) signals, and sensory evaluation during human oral processing
• Correlation Analysis: Establishing relationships between instrumental measurements and human sensory data using statistical methods

Table 1: Correlation Between Biomimetic Probe Measurements and Sensory Attributes

Probe Type	Test Speed (mm/s)	Sensory Attribute	Correlation Coefficient (rs)
M1	10.0	Hardness	0.8857
M2	1.0	Fracturability	0.9714
Conventional P/50	Multiple speeds	Hardness	Significantly lower
Conventional HPD	Multiple speeds	Fracturability	Significantly lower

The results demonstrated that the biomimetic probes significantly outperformed conventional probes, with the M1 probe at 10.0 mm/s showing the highest correlation with sensory hardness (rs = 0.8857), while the M2 probe at 1.0 mm/s showed near-perfect correlation with sensory fracturability (rs = 0.9714) [27]. This study established that specific probe configurations optimized for particular textural attributes provide more accurate predictions of human sensory responses than one-size-fits-all conventional approaches.

Two-Bite Testing and Chewing Simulation

For certain applications, particularly in pharmaceutical development for medicated chewing gums (MCGs), more sophisticated simulation approaches have been developed. The two-bite test represents an advanced methodology that better replicates the initial mastication cycles where key texture perceptions and drug release processes occur [28].

The experimental protocol for two-bite testing involves:

• Testbed Configuration: A bio-engineered system with compression and torsion capabilities
• Parameter Adjustment: Controlled variation of FMA (0°, 25°, 29.1°) and BA (0°, 8°) to simulate different jaw morphologies
• Force Measurement: Precise monitoring of crushing and shearing forces during simulated bites
• Release Quantification: Measurement of active pharmaceutical ingredient (API) release from medicated chewing gums

This approach recognizes that human chewing efficiency depends not only on force application but also on the specific kinematics of jaw movement and dental morphology [28]. The two-bite test parameters effectively simulate the mechanochemical processes that disrupt mechanical bonds in chewing gums, thereby releasing active ingredients—a crucial consideration for pharmaceutical applications where consistent dosing depends on standardized chewing efficiency [28].

Machine Learning and Predictive Modeling

Beyond physical simulation, computational approaches have emerged to bridge the gap between instrumental measurements and sensory perception. Research from Purdue University's Food Science department has developed an artificial intelligence model that predicts texture perception based on physical properties measurable in laboratory settings [12].

The methodology employs a sensory-based autoencoder—a specialized neural network designed to learn how humans perceive texture from limited datasets [12]. This approach is particularly valuable because it can work with small sample sizes, using cross-validation techniques to ensure reliability without overfitting [12]. The model was trained using data from just a few bouillon samples yet demonstrated remarkable predictive capability for perceived thickness and other mouthfeel attributes [12].

This machine learning application enables food scientists to predict sensory outcomes without continuous rounds of product development and human testing, significantly accelerating the development process while reducing costs [12]. The model has particular relevance for designing foods for specific populations, such as those with swallowing difficulties, where precise texture control is critical for safety and acceptability [12].

Experimental Protocols and Methodologies

Standardized TPA Protocol with Biomimetic Probes

Implementing a standardized TPA protocol with biomimetic probes requires careful attention to experimental parameters that influence measurement accuracy and correlation with sensory data.

Table 2: Optimal Test Parameters for Biomimetic TPA

Parameter	Recommended Setting	Impact on Measurement
Test Speed	0.1-10.0 mm/s	Higher speeds (10.0 mm/s) optimize hardness correlation; lower speeds (1.0 mm/s) optimize fracturability correlation [27]
Probe Selection	M1 or M2 biomimetic	M1 optimal for hardness; M2 optimal for fracturability [27]
Sample Preparation	Standardized size and orientation	Ensures consistent compression dynamics and reproducible results
Data Acquisition	Force-time curve recording	Enables extraction of multiple texture parameters (hardness, cohesiveness, springiness, etc.)
Environmental Control	Constant temperature and humidity	Prevents sample property variations during testing

The experimental workflow begins with probe selection based on the target sensory attribute, followed by calibration at the appropriate test speed [27]. Samples should be prepared with consistent dimensions and orientation relative to the probe. During testing, the force-time curve is recorded and analyzed for key parameters, with particular attention to the first and second compression cycles that simulate initial bites [27].

Correlation with Human Sensory Evaluation

To establish predictive relationships between instrumental measurements and human perception, a standardized sensory evaluation protocol must be implemented concurrently with TPA:

• Sensory Panel Training: Panelists are trained using reference standards for specific texture attributes (hardness, fracturability, chewiness, etc.)
• Sample Presentation: Samples are presented in randomized order under controlled conditions to minimize bias
• Sensory Scale Utilization: Panelists rate intensity of specific attributes using structured scales (e.g., 1-15 point intensity scales)
• Statistical Analysis: Correlation analysis (e.g., Spearman rank correlation) between instrumental and sensory data establishes predictive relationships

This methodology enabled the high correlations (rs = 0.8857-0.9714) demonstrated in the hazelnut study, validating the biomimetic approach to TPA [27].

Applications and Industry Implications

Food Product Development and Optimization

The advanced TPA methodologies discussed have significant implications for food product development, particularly in addressing evolving consumer trends related to mouthfeel. Tate & Lyle's "The Future of Mouthfeel" report identifies nine key trends for 2025 that are shaping product development priorities [29]:

• Hyper-Crunch: Extension of crunchy textures from savory to sweet categories with exotic twists
• Air-textured Foods: Puffed, popped, and bubbly sensations that evoke indulgence and lightness
• Multi-layered Mouthfeel: Combinations of contrasting textures (crunchy/soft, dry/wet) for novel experiences
• Next-gen Natural: Cleaner labels while maintaining expected mouthfeels through natural texturants
• Shelf-proof Mouthfeel: Maintenance of desirable textures through extended shelf life and processing
• Mouthfeel Mimicry: Replication of traditional textures in plant-based and alternative products
• Veggie Celebration: Preservation of fresh, vibrant, crunchy vegetable textures in convenient formats
• Blissfully Boosted: Incorporation of functional ingredients without compromising indulgence
• Climate-proof Food: Maintenance of texture stability under varying temperature conditions

Advanced TPA enables manufacturers to systematically address these trends by quantitatively measuring how formulation changes affect textural properties, reducing reliance on costly and time-consuming sensory panels throughout the development process [29].

Pharmaceutical Applications of Chewing Simulation

In pharmaceutical science, medicated chewing gums (MCGs) represent an advanced drug delivery method with significant advantages over conventional tablets, including avoidance of first-pass metabolism, improved bioavailability, and faster onset of action [28]. However, the development of MCGs has been hampered by the lack of reliable in vitro testing methods that accurately simulate human chewing to predict drug release [28].

The two-bite testing approach addresses this limitation by incorporating critical parameters of human mastication, including:

• Jaw physiology and dental morphology effects on crushing and shearing efficiency
• The role of FMA and BA angles in controlling crushing/shear ratios
• Saliva simulation and its effect on API release kinetics
• Chewing efficiency standardization for consistent dosing

This methodology enables more predictive assessment of drug release profiles before human trials, potentially reducing development time and costs while improving product performance [28]. With global markets for common MCG ingredients like aspirin and vitamin C worth billions annually, even modest improvements in formulation efficiency through better testing could yield significant economic benefits [28].

The Scientist's Toolkit: Essential Research Reagents and Equipment

Table 3: Key Research Tools for Advanced TPA Studies

Tool/Reagent	Function/Application	Technical Specifications
Biomimetic Molar Probes (M1, M2)	Simulation of human chewing mechanics with anatomical accuracy	Based on human molar morphology; customizable for specific food/pharmaceutical applications [27]
Texture Analyzer with Variable Speed Control	Application of controlled compression forces with precise kinematics	Test speed range: 0.1-10.0 mm/s or broader; compatible with biomimetic probes [27]
Humanoid Chewing Robot	Reproduction of complex masticatory cycles for predictive testing	Incorporates FMA and BA parameters; simulates full chewing sequence [28]
Surface Electromyography (EMG)	Measurement of muscle activity during human chewing for validation	Multi-channel recording; synchronized with force measurement [27]
Sensory-based Autoencoder (AI Model)	Prediction of sensory texture from instrumental data	Neural network architecture; effective with small datasets [12]
Saliva Simulation System	Reproduction of oral chemical environment for drug release studies	Controlled composition, pH, and flow rate [28]
Particle Size Analyzer	Quantification of food breakdown during mastication	Laser diffraction or sieve analysis; correlates with sensory perception [27]

Visualizing Experimental Workflows and Relationships

TPA-Sensory Correlation Workflow

Biomimetic TPA-Sensory Correlation Workflow

Mouthfeel Perception Pathway

Mouthfeel Perception Pathway

Future Directions and Research Opportunities

The field of instrumental TPA continues to evolve with several promising research directions emerging. The integration of machine learning and artificial intelligence represents a paradigm shift, enabling predictive modeling of sensory outcomes from instrumental data even with limited sample sizes [12]. This approach is particularly valuable for optimizing texture in specialized nutritional products for populations with specific needs, such as the elderly or those with swallowing difficulties [12].

Advanced humanoid chewing robots with sophisticated control of FMA and BA parameters offer potential for standardized chewing efficiency measurement across food and pharmaceutical applications [28]. These systems could establish reproducible in vitro methodologies that account for the complex biomechanics of human mastication, addressing a significant limitation in current MCG testing protocols [28].

The growing consumer and industry focus on mouthfeel trends—including multi-textured experiences, climate-resistant textures, and clean-label texturants—creates ongoing needs for more sophisticated and predictive TPA methodologies [29]. Research addressing these trends will likely drive further innovation in biomimetic testing approaches that bridge the persistent gap between instrumental measurement and human sensory experience.

As these technologies mature, the establishment of standardized protocols incorporating biomimetic principles will enhance reproducibility across laboratories and enable more efficient product development cycles through improved predictive capability of instrumental methods.

In the multifaceted field of food science, texture and mouthfeel are critical determinants of consumer acceptance and product quality. While taste and aroma have traditionally received significant research attention, the tactile experience of food—governed by its mechanical properties—is equally vital [1]. This whitepaper delineates the five core mechanical parameters of food texture: Hardness, Cohesiveness, Adhesiveness, Springiness, and Fracturability. These parameters are fundamental to understanding and quantifying the sensory perception known as mouthfeel, which is defined as the complex physical, tactile, and textural sensations experienced in the oral cavity during consumption [1]. For researchers and product development professionals, mastering these parameters is essential for designing foods, optimizing processing methods, and even developing pharmaceutical formulations with specific release characteristics.

The perception of mouthfeel is a complex process involving the somatosensory system, including the trigeminal nerve, and is influenced by oral processing and saliva composition [1]. Although sensory panels are a gold standard for evaluation, they are resource-intensive. Instrumental methods, particularly Texture Profile Analysis (TPA), provide a robust, objective, and reproducible alternative to correlate mechanical properties with sensory experience [30]. This guide provides an in-depth technical examination of these core parameters, their experimental determination via TPA, and their critical role in advancing food texture and mouthfeel research.

Theoretical Foundations of Food Texture

The Role of Mouthfeel in Sensory Perception

Mouthfeel is not a singular attribute but a multidimensional sensory perception that plays a significant role in overall flavor perception and consumer liking [1]. It arises from the integration of signals from multiple sensory pathways, including the gustatory and, importantly, the trigeminal systems, which detect tactile, thermal, and irritant stimuli [1]. The mechanical and structural properties of food, which can be instrumentally measured, are expressed as texture, and this texture is a primary driver of the mouthfeel experience [1]. In some products, texture can be more significant to consumer preference than flavor itself [1].

Table 1: Key Sensory Systems Involved in Mouthfeel Perception

Sensory System	Physiological Basis	Contributions to Mouthfeel
Somatosensory	Trigeminal nerve endings in oral cavity	Detection of tactile, thermal, and mechanical stimuli (e.g., grittiness, cooling)
Gustatory	Taste receptors on tongue	Basic tastes (sweet, salty, sour, bitter, umami); can alter rheological properties
Olfactory	Olfactory receptors in nasal cavity	Perception of aroma from volatile compounds
Mechanoreception	Pressure-sensitive receptors	Detection of force-related properties like hardness and viscosity

From Physical Properties to Sensory Perception

The relationship between a food's intrinsic physical properties and the resulting sensory perception is complex. Characteristics such as creaminess, crunchiness, and lightness are influenced by the food's composition and structure [1]. For instance, the particle size and distribution in chocolate directly affect its melting behavior and smoothness in the mouth [1]. During oral processing, food undergoes compression, shearing, and mixing with saliva, which continuously alters its properties and the resulting tactile feedback. This dynamic process is what instrumental TPA aims to simulate mechanically.

Texture Profile Analysis is a double compression test that simulates the action of biting a food sample. A force-time curve is generated from which quantitative parameters are extracted that correlate well with sensory evaluation [30]. This method provides a comprehensive mechanical fingerprint of a food sample.

The TPA Test Workflow

The standard TPA test involves a probe compressing a bite-sized piece of food twice, with a brief period of withdrawal between compressions, mimicking the first and second bites [30]. The test can be broken down into several stages, as visualized below.

Detailed Examination of Core Parameters

The force-time curve generated from a TPA test is rich with data. The following diagram maps the key parameters to their specific locations on a generic TPA curve, providing a visual reference for the subsequent detailed explanations.

Table 2: Core TPA Parameters and Their Definitions

Parameter	Definition	Sensory Correlation	Typical Units
Hardness	The maximum force required during the first compression cycle [30].	The force needed to compress a food between the teeth (firmness) [30].	Newtons (N) or grams (g)
Fracturability	The force at the first significant break in the curve during the first compression, if present [30].	The tendency of a material to fracture or crumble (brittleness, crunchiness).	Newtons (N) or grams (g)
Adhesiveness	The negative force area recorded after the first compression during probe withdrawal [30].	The work required to overcome the attractive forces between the food surface and the oral surfaces (stickiness).	Newton-seconds (N·s) or Joule (J)
Springiness	The ratio of the time difference during the second compression to that during the first compression [30].	The rate at which a deformed material returns to its original shape after the deforming force is removed.	Dimensionless
Cohesiveness	The ratio of the positive force area during the second compression to that during the first compression [30].	The strength of the internal bonds holding the food's structure together.	Dimensionless

Hardness

• Experimental Protocol: Within the TPA test, hardness is directly measured as the peak force (at anchor 2) encountered during the first compression cycle (Stage 1) [30]. A higher peak force indicates a firmer material. The test requires a standardized sample size and shape to ensure reproducibility.
• Research Significance: Hardness is a primary indicator of product quality and freshness in many foods, such as cheeses, fruits, and gels. It is crucial for ensuring product consistency and meeting consumer expectations for specific product categories.

Fracturability

• Experimental Protocol: Fracturability is identified as a peak that occurs within the first compression cycle, before the highest peak (hardness) is reached [30]. It is not present in all materials; for example, soft and elastic materials like bread may not exhibit a fracturability peak. Its absence is indicated as "N/A" in results [30].
• Research Significance: This parameter is critical for products where a crisp or crunchy texture is desired, such as crackers, biscuits, and certain hard cheeses. A lack of expected fracturability can be perceived as a quality defect, like stale snacks.

Adhesiveness

• Experimental Protocol: Adhesiveness is quantified by measuring the negative force area on the TPA graph that occurs as the probe withdraws after the first compression (Stage 2) [30]. This negative force represents the work required to pull the probe away from the sample. A larger negative area indicates a stickier material.
• Research Significance: Adhesiveness is a key parameter in products like sauces, pastes, and certain confectioneries. High adhesiveness can be desirable (e.g., in a rich cheese sauce) or undesirable (e.g., causing a starchy mouthfeel), guiding formulation adjustments.

Springiness

• Experimental Protocol: Springiness is calculated as the ratio of the time difference during the second compression to the time difference during the first compression (Time diff 4:5 / Time diff 1:2) [30]. It measures how much the sample recovers its height between the two compressions. A perfect spring would have a ratio of 1.
• Research Significance: Springiness relates to the elasticity and resilience of a food. It is vital for the quality of gummy candies, certain baked goods, and processed meats, influencing the product's "bounce" and chew characteristics.

Cohesiveness

• Experimental Protocol: Cohesiveness is derived from the ratio of the positive force area during the second compression to the positive force area during the first compression (Area 4:6 / Area 1:3) [30]. This ratio reflects the sample's ability to withstand a second deformation relative to the first, indicating the strength of its internal bonds.
• Research Significance: Cohesiveness describes how well a product holds together. Low cohesiveness is found in crumbly foods (e.g., some cakes), while high cohesiveness is found in cohesive foods (e.g., cheese). It is a fundamental parameter for predicting structural integrity and eating quality.

Derived Parameters: Gumminess and Chewiness

From the core parameters, two other critical textural attributes can be calculated:

• Gumminess: Defined as the product of Hardness × Cohesiveness [30]. This parameter applies to semi-solid foods and describes the energy required to disintegrate a food until it is ready for swallowing.
• Chewiness: Defined as the product of Hardness × Cohesiveness × Springiness (or Gumminess × Springiness) [30]. This parameter applies to solid foods and represents the energy required to masticate a solid food until it is ready for swallowing.

Table 3: TPA Parameter Manifestation in Different Food Types

Food Type	TPA Curve Characteristics	Typical Parameter Profile
Hard & Brittle	Steep initial rise, high first peak, much smaller second area [30].	High Hardness & Fracturability, Low Cohesiveness & Springiness [30].
Soft & Elastic	Gradual rise to first peak, similar first and second areas [30].	Low Hardness, High Cohesiveness, Springiness, and Adhesiveness [30].
Gummy/Sticky	Pronounced negative area; peaks may not be distinct [30].	High Adhesiveness and Cohesiveness, moderate Hardness.
Firm & Cohesive	Similar first and second peaks; minimal negative area [30].	High Hardness and Cohesiveness, moderate Springiness, low Adhesiveness [30].

The Scientist's Toolkit: Essential Reagents and Materials

To conduct reproducible and accurate TPA, a standardized set of equipment and materials is required. The following table details key components of a TPA research setup.

Table 4: Essential Research Toolkit for Texture Profile Analysis

Item	Function & Importance in TPA
Texture Analyzer	The core instrument that drives the probe and measures force with high precision. It must be calibrated regularly.
Compression Probe	The component that physically interacts with the sample. Cylindrical (e.g., 75mm) probes are common, but selection depends on sample and standard method.
Data Acquisition Software	Specialized software that controls the instrument, records the force-time data, and automatically calculates the TPA parameters.
Standard Reference Materials	Materials with known mechanical properties (e.g., acrylic or rubber standards) used for regular instrument verification and calibration.
Sample Preparation Tools	Corers, cutters, and molds to create samples of uniform size and shape, which is critical for obtaining reproducible results.

The core mechanical parameters of Hardness, Cohesiveness, Adhesiveness, Springiness, and Fracturability provide an objective, quantitative framework for deconstructing the complex sensory experience of food texture and mouthfeel. Texture Profile Analysis serves as a powerful bridge between physical measurement and human perception, enabling researchers and product developers to predict sensory outcomes, troubleshoot quality issues, and innovate with precision. As the demand for tailored food experiences and non-alcoholic products like beer grows—where mouthfeel is paramount—the rigorous application of these principles will be indispensable for advancing product quality and consumer satisfaction in the food and pharmaceutical industries.

The sensory experience of food and oral pharmaceuticals is a critical determinant of consumer acceptance and compliance. This experience, encompassing texture, mouthfeel, and taste, is a complex, dynamic phenomenon that occurs during oral processing. Understanding these properties requires a multi-faceted instrumental approach that goes beyond human sensory panels. This whitepaper details three advanced instrumental toolkits—rheology, tribology, and electronic tongues—that are central to modern food and pharmaceutical research. These methods provide the quantitative, objective, and predictive data necessary to decode the intricate physics and chemistry of oral sensation, thereby enabling the rational design of products with superior sensory profiles [2] [31] [1].

Rheology: The Study of Flow and Deformation

Rheology is the science of the deformation and flow of matter. In the context of oral processing, it is fundamental to understanding the initial mechanical properties of a product upon entry into the mouth [2] [31].

Fundamental Principles and Key Parameters

Food materials are often viscoelastic, meaning they exhibit both viscous (liquid-like) and elastic (solid-like) properties. Key rheological parameters include:

• Shear Stress (σ): The force per unit area required to achieve flow.
• Shear Rate (γ̇): The velocity gradient applied during flow.
• Viscosity (η): The resistance to flow, defined as the ratio of shear stress to shear rate (η = σ/γ̇).
• Storage Modulus (G′): Quantifies the solid-like, elastic component of a material; it represents the energy stored and recovered per cycle of deformation.
• Loss Modulus (G″): Quantifies the liquid-like, viscous component; it represents the energy lost as heat per cycle of deformation [2] [32].

Many complex fluids like yogurt are shear-thinning, meaning their viscosity decreases with increasing shear rate, and thixotropic, meaning their viscosity decreases over time under a constant shear rate, with a partial recovery once the shear is removed [32].

Correlating Rheology to Texture Attributes

Rheological measurements are powerfully correlated with specific sensory texture attributes. The table below summarizes key relationships.

Table 1: Correlation between rheological parameters and sensory texture attributes.

Rheological Parameter	Measured Using	Correlated Sensory Attribute	Application Example
Storage Modulus (G′)	Oscillatory Rheometry	Firmness, Gel Strength	Yogurt gel structure formation [32].
Apparent Viscosity	Rotational Rheometry	Thickness, Creaminess	Perception of thickened beverages and sauces [2].
Yield Stress (τ_yield)	Controlled Stress Rheometry	Spreadability, Spoonability	The stress required to initiate flow in products like Greek yogurt [32].

Experimental Protocol: Oscillatory Rheology of a Yogurt Gel

Objective: To characterize the viscoelastic properties of a yogurt sample during a simulated gelation process or during oral processing.

Materials:

• Controlled-stress/strain rheometer with a parallel plate or cup-and-bob geometry
• Peltier temperature control unit
• Yogurt sample

Methodology:

• Loading: Carefully load the yogurt sample onto the rheometer's lower plate, ensuring minimal structural disruption.
• Gap Setting: Bring the upper plate to a defined measurement gap (e.g., 1 mm) and trim excess sample.
• Temperature Control: Set the Peltier system to a relevant temperature (e.g., 37°C to simulate mouth temperature).
• Amplitude Sweep: Perform an amplitude sweep at a fixed frequency (e.g., 1 Hz) to determine the Linear Viscoelastic Region (LVR), where G′ and G″ are independent of strain.
• Frequency Sweep: Conduct a frequency sweep (e.g., 0.1 to 100 rad/s) at a strain within the LVR to monitor the mechanical spectrum (G′ and G″ vs. frequency).
• Data Analysis: In a stable gel, G′ is typically greater than G″ across the frequency range, indicating a solid-dominant structure. The relative values and their frequency dependence provide insight into gel strength and stability [32].

Tribology: The Study of Friction and Lubrication

While rheology dominates the initial stages of oral processing, tribology has emerged as a crucial tool for understanding the thin-film, lubrication-dominated regime that occurs later, as the food bolus is manipulated between the tongue and palate [2] [31]. This directly relates to attributes like smoothness, creaminess, and astringency.

Fundamental Principles and Key Parameters

Tribology is defined as the science of interacting surfaces in relative motion. The key parameter measured is:

• Coefficient of Friction (μ): The ratio of the frictional force to the normal force. A lower coefficient of friction is associated with smoother, creamier sensations [33] [31].

To mimic oral conditions, tribological setups now use soft, elastomeric surfaces like polydimethylsiloxane (PDMS) instead of traditional steel-on-steel contacts [33].

Correlating Tribology to Mouthfeel Attributes

Tribology provides unique insights that rheology cannot, particularly for surface-related sensations.

Table 2: Correlation between tribological parameters and mouthfeel attributes.

Tribological Parameter	Measured Using	Correlated Sensory Attribute	Application Example
Coefficient of Friction (μ) vs. Speed	Soft Tribometer	Creaminess, Smoothness, Astringency	Distinguishing low-fat and high-fat dairy products; evaluating astringency in beverages [2] [33].
Stribeck Curve	Soft Tribometer	Dynamic Mouthfeel Perception	Mapping the transition from boundary to mixed to hydrodynamic lubrication regimes during oral processing [31].

Experimental Protocol: Bio-tribology of an Orodispersible Film (ODF)

Objective: To evaluate the lubricating properties and disintegration behavior of ODFs using a physiologically relevant tribological setup.

Materials:

• Tribometer with a flat-on-flat or ball-on-disk configuration
• PDMS pads (to mimic the soft tongue surface)
• Simulated Salivary Fluid (SSF)
• Orodispersible Film (ODF) samples

Methodology:

• Surface Preparation: Attach a flat PDMS pad to the stationary lower stage and a harder acrylic upper plate to the moving arm to mimic the tongue-palate configuration.
• Lubricant Application: Apply a sufficient volume of SSF to the PDMS surface.
• Film Placement: Place the ODF sample onto the SSF-lubricated PDMS.
• Testing: Apply a defined normal load (e.g., 1-5 N) and run a reciprocating or rotational test, measuring the coefficient of friction over time as the film hydrates and disintegrates.
• Data Analysis: A rapid decrease in friction indicates quick disintegration and good lubrication, suggesting a potentially favorable mouthfeel. A higher, stickier friction profile suggests poor oral perception [33].

The following diagram illustrates the typical trajectory of a food's physical properties as it is processed in the mouth, highlighting the domains where rheology and tribology are most relevant.

Electronic Tongues: Biomimetic Taste Sensors

Electronic tongues (e-tongues) are analytical sensor array systems designed to objectively quantify taste [34] [35]. They are particularly valuable for screening bitter drugs or evaluating taste-masked formulations without relying on human panels.

Fundamental Principles and Sensor Types

E-tongues use an array of non-specific, cross-sensitive sensors that generate a composite signal pattern ("fingerprint") for a liquid sample. The most common operating principle is potentiometry, where the potential difference between a working electrode and a reference electrode is measured [34] [36]. Sensor membranes are often made from lipids/PVC or chalcogenide glasses to mimic the response of biological taste buds [34].

Key Applications and Performance

E-tongues have demonstrated high predictive accuracy in various applications. Table 3: Performance and applications of electronic tongues.

Application	Sensors Used	Reported Accuracy	Reference
Taste Masking of Bitter Drugs	Lipid/Polymer Membranes	>80% correlation with human panels	[34]
Basic Taste Identification (Sweet, Sour, Salty, Bitter)	Graphene Oxide Membranes	72.5% - 87.5%	[37]
Complex Beverage Analysis (Coffee, Cola)	Graphene Oxide Membranes	~96%	[37]
Food Quality & Safety Monitoring	Sensor Arrays (Potentiometric, Voltammetric)	80% - 96% in various applications	[35]

Emerging Technology: The Bionic "Chili-Meter"

Beyond basic tastes, novel e-tongues are being developed for specific sensations. For example, a gel-based artificial tongue using milk proteins (inspired by milk's ability to soothe spiciness) has been created to measure pungency from capsaicin in chili peppers. The device's electrical current drops as capsaicin binds to the proteins, providing a quantitative "spiciness" scale that closely matches human sensory rankings [38].

The workflow of an electronic tongue, from sensing to pattern recognition, is detailed below.

Integrated Research Reagents and Materials

Successful application of these advanced tools requires specific, well-characterized materials. The following table lists key reagents and their functions in experimental protocols.

Table 4: Essential research reagents and materials for texture and taste analysis.

Reagent/Material	Function/Application	Relevant Technique
Polydimethylsiloxane (PDMS)	Elastomer used to create soft, tongue-like surfaces in tribological studies.	Tribology [33]
Simulated Salivary Fluid (SSF)	Aqueous solution mimicking the ionic composition and lubricating properties of human saliva; crucial for physiologically relevant testing.	Tribology, E-Tongue [33]
Lipid/PVC Membranes	Sensing materials in potentiometric e-tongues; interact with tastants similarly to biological membranes.	Electronic Tongue [34]
Graphene Oxide Membranes	Nanomaterial used in advanced e-tongues; ions from tastants pass through nanochannels, creating unique electrical signatures.	Electronic Tongue [37]
Sodium Carboxymethyl Cellulose (CMC)	A hydrocolloid used to create model fluids with controlled viscosity and as a polymer base for Orodispersible Films (ODFs).	Rheology, Tribology [33]

The multifaceted challenge of understanding food texture and mouthfeel requires a sophisticated, multi-modal toolkit. Rheology provides foundational knowledge of a product's mechanical properties and initial breakdown. Tribology is indispensable for unraveling the later-stage, thin-film lubrication behavior that dictates key mouthfeel attributes like smoothness and creaminess. Meanwhile, electronic tongues offer a powerful, objective means for quantifying taste, a sensory modality deeply intertwined with texture perception. The future of this field lies in the intelligent integration of these techniques, alongside emerging technologies like micro-analytical and biomimetic sensing, to build comprehensive predictive models of sensory perception. This integrated approach will ultimately accelerate the rational design of healthier, more appealing, and patient-friendly food and pharmaceutical products.

In the specialized field of food texture and mouthfeel research, human sensory panels function as precision instruments, providing critical data that instrumental methods cannot fully capture. The complex, multidimensional nature of mouthfeel—encompassing tactile, mechanical, and surface properties like viscosity, crunchiness, astringency, and creaminess—makes human perception indispensable for comprehensive understanding [1]. Sensory panels bridge the gap between quantitative physical measurements and subjective human experience, allowing researchers to decode how chemical and structural properties of food translate into perceptual qualities during oral processing [1]. The reliability of this data hinges entirely on rigorous panel management, including systematic selection, standardized training methodologies, and precise intensity scaling techniques implemented within controlled testing environments.

The terminology governing sensory evaluation distinguishes between several types of assessors based on their qualification level. Naive assessors are persons who do not meet any specific criterion and may include regular consumers. Initiated sensory assessors have previously participated in sensory analysis, while selected assessors demonstrate special skills and suitability through screening. The highest qualification level, expert sensory assessors, represents individuals who consistently perform reliable, repeatable sensory analyses based on trained sensory acuity, continuous practice, and accumulated experience [39]. These distinctions are critical for structuring appropriate training protocols and setting performance expectations for texture and mouthfeel evaluation.

Sensory Panel Selection and Training Framework

Panel Selection and Screening

Establishing a competent sensory panel begins with meticulous selection and screening processes designed to identify individuals with the inherent sensory acuity and cognitive capabilities necessary for texture discrimination. The selection procedure must assess potential panelists for their ability to detect basic tastes, recognize texture variations, and describe sensory experiences with sufficient vocabulary. Candidates should be screened for color blindness, anosmia (inability to smell), ageusia (inability to taste), and any food allergies that might compromise testing or safety [40]. Additionally, selection should consider practical factors including availability, motivation, communication skills, and ability to work within a group dynamic [39].

Sensory panels can be constituted from different sources, each with distinct advantages. Internal panels comprise employees recruited from within an organization across various departments. External panels are recruited from outside the organization, while mixed panels combine both internal and external participants [39]. For analytical sensory tests focused on texture profiling, selected assessors or expert sensory assessors are required, as they must operate with the reliability and precision of laboratory instruments, delivering valid, reliable, objective, and reproducible results [39]. The panel size varies depending on the test methodology, with descriptive tests typically requiring 5-12 highly trained panelists, while discrimination tests may need 10-50 participants depending on the specific protocol [40].

Table 1: Sensory Assessor Types and Their Applications in Texture Research

Assessor Type	Training Requirement	Primary Use Cases	Typical Panel Size
Naive Assessor	No formal training	Hedonic testing, consumer preference	40+ participants
Initiated Assessor	Basic orientation	Simple discrimination tests	10-30 participants
Selected Assessor	Screening and foundational training	Descriptive analysis, quality control	8-15 participants
Expert Sensory Assessor	Extended training with performance monitoring	Quantitative Descriptive Analysis, texture profiling	5-12 participants

Systematic Training Methodology

Training transforms selected individuals into proficient sensory assessors capable of detecting subtle texture variations and quantifying their intensity with consistency. The European standard DIN EN ISO 8586:2014-05 outlines comprehensive guidelines for sensory assessor training, emphasizing a modular approach where training units are selected based on specific project requirements and desired qualification outcomes [39]. Training for texture-focused panels should progress through three developmental phases: foundational sensory skills, attribute-specific calibration, and methodological application.

For mouthfeel and texture profiling, training must specifically focus on the description of sensory characteristics, recognition of texture intensities, and quantitative rating of attributes using standardized scales [39]. This involves exposing trainees to reference standards that exemplify specific texture attributes at various intensity levels. For instance, panelists might compare viscosity using liquid standards with known rheological properties, or evaluate crispness using products with controlled water activity and microstructure. The training incorporates progressive exercises that build discrimination skills through triangle tests, paired comparisons, and ranking tasks specifically designed for texture attributes [39] [40].

Performance monitoring during training is essential, measuring repeatability (consistency within an individual's assessments), reproducibility (agreement between different panelists), and discrimination ability (sensitivity to detect differences) [39]. Training continues until panelists demonstrate statistical reliability in their evaluations, with regular refresher sessions to maintain calibration. For texture profiling, this often requires 40-120 hours of training depending on product complexity and the panel's previous experience [39].

Intensity Scaling Methods for Texture and Mouthfeel

Intensity scaling provides the quantitative foundation for sensory evaluation, transforming subjective perceptions into measurable data. Various scaling approaches serve different research objectives in texture analysis, each with distinct advantages and limitations for capturing mouthfeel attributes.

Category scales utilize a limited number of ordered categories with verbal anchors at points along the scale. These are particularly useful for rapid profiling methods and consumer panels where simplicity is advantageous. Line scales (visual analog scales) present participants with continuous lines anchored at extremes (e.g., "not creamy" to "extremely creamy"), allowing for infinite gradations between endpoints. The 9-point hedonic scale specifically measures liking and acceptance, ranging from "dislike extremely" to "like extremely" [40]. For texture profiling that requires high precision, the Spectrum Method uses universal intensity scales calibrated with specific reference products that represent fixed points along the scale, enabling cross-panel comparisons and absolute intensity measurements [41].

The selection of appropriate scaling methodology depends on research objectives, panel training level, and required data precision. Highly trained panels typically use line scales or Spectrum scales for detailed texture mapping, while consumer panels employ category scales or hedonic scales for acceptance testing. Recent methodological advancements include Preferred Attribute Elicitation (PAE), a rapid technique that allows consumers to collaboratively identify, measure, and prioritize key attributes while providing hedonic insights, effectively integrating descriptive and affective dimensions of sensory perception [41].

Table 2: Intensity Scaling Methods for Texture and Mouthfeel Evaluation

Scale Type	Structure	Data Output	Best Applications in Texture Research
Category Scale	5-9 ordered categories with verbal anchors	Ordinal	Screening tests, consumer perception studies
Line Scale	100mm continuous line with end anchors	Continuous/Interval	Descriptive analysis, trained panel profiling
9-point Hedonic Scale	9 categories from "dislike extremely" to "like extremely"	Ordinal	Product acceptance, preference mapping
Spectrum Scale	Universal scale with fixed reference points	Ratio	Cross-study comparisons, quantitative profiling
PAE Ranking	Attribute prioritization and intensity scoring	Ordinal/Ranking	Rapid profiling, consumer-driven attribute selection

Standardized Testing Protocols and Environmental Controls

Experimental Protocols for Texture Analysis

Sensory analysis employs structured experimental protocols designed to answer specific research questions while controlling for bias and external influences. These methods fall into three primary categories, each with distinct applications in texture and mouthfeel research.

Discrimination tests determine whether perceptible differences exist between products. The triangle test presents three samples—two identical and one different—requiring panelists to identify the odd sample. This method is ideal for detecting texture changes after ingredient substitution or process modification. The duo-trio test provides a reference sample followed by two test samples (one matching the reference), requiring identification of the matching sample. Paired comparison tests present two samples with panelists indicating which has more of a specific characteristic (e.g., crunchiness, viscosity) [40]. These methods are particularly valuable for establishing detection thresholds for texture variations.

Descriptive tests provide comprehensive qualitative and quantitative profiles of products' sensory attributes. Simple descriptive tests use predetermined attribute lists with intensity ratings. Quantitative Descriptive Analysis (QDA) employs trained panels to identify and quantify sensory attributes using reproducible intensity scales, generating detailed sensory profiles. Texture Profile Analysis (TPA) specifically evaluates mechanical characteristics (hardness, cohesiveness, springiness, etc.) through standardized chewing protocols [40] [41]. These methods are essential for mapping the complete mouthfeel experience and correlating sensory properties with instrumental measurements.

Affective tests measure consumer acceptance and preference. Paired preference tests directly compare two products, while hedonic rating tests use the 9-point hedonic scale to measure degree of liking for individual products. Ranking tests order multiple products based on preference or specific attributes [40]. These methods determine how texture attributes influence consumer acceptance and market success.

Environmental Standardization and Controls

Standardized testing environments are critical for reliable sensory data, particularly for texture evaluation where external cues can influence perception. International standard DIN EN ISO 8589:2010+A1:2014 specifies requirements for sensory test rooms designed to minimize distraction and psychological influences on judgment [39]. The testing environment must control several key factors to ensure data integrity.

Lighting must be uniform, adjustable, and virtually shadow-free, with artificial lighting simulating natural light at color temperatures of 6,000-6,500 K (Kelvin) to enable objective appearance assessment. When color evaluation might bias texture assessment, red light or color filters can mask color differences [39]. Acoustic control is essential, as noise affects texture perception; testing rooms require soundproofing to eliminate external noise and minimize internal sounds from movement or equipment [39] [40]. Temperature and humidity must be controlled consistently, as they affect both sample properties and panelist comfort [39].

Sample presentation follows strict protocols to minimize bias. Samples are coded with random 3-digit or 3-letter codes to prevent brand or formulation identification. Serving order is randomized across panelists to control for sequence effects. Standardized serving vessels, typically white to prevent color influence, ensure consistent evaluation conditions. Panelists work in individual testing booths to prevent communication and influence during evaluation [42] [40]. For texture analysis, specific instructions regarding bite size, chewing protocol, and evaluation timing may be standardized to reduce variability.

The Scientist's Toolkit: Essential Materials and Reagent Solutions

Table 3: Essential Research Materials for Sensory Texture Analysis

Material/Reagent	Function in Sensory Analysis	Application in Texture Research
Reference Standards	Calibrate panelists' intensity scales for specific attributes	Establish consistent perception of texture attributes like viscosity, hardness, crispness
Basic Taste Solutions	Validate taste sensitivity during screening and training	Ensure panelists can discriminate taste-texture interactions (e.g., sweetness-thickness)
Texture References	Provide physical benchmarks for texture attributes	Anchor scales for specific products (e.g., creaminess, crunchiness, astringency)
Neutralizing Agents	Clear palate between samples to prevent carryover	Plain crackers, water, unsweetened apples for removing residual mouthfeel
Standardized Serving Ware	Present samples consistently across evaluations	White plates, cups, spoons to minimize visual bias during texture assessment
Sensory Software	Collect, manage, and analyze sensory data	Computerized data acquisition for time-intensity studies and descriptive analysis

Human sensory panels remain indispensable tools for advancing food texture and mouthfeel research, providing irreplaceable perceptual data that bridges the gap between instrumental measurements and consumer experience. The validity of this data depends entirely on rigorous implementation of standardized methodologies encompassing panelist selection, progressive training protocols, controlled testing environments, and appropriate intensity scaling techniques. As research in this field evolves, emerging technologies like machine learning are showing promise for predicting texture perception based on physical properties [12], yet these computational approaches still require validation through well-designed sensory panels. Similarly, rapid sensory profiling techniques like Preferred Attribute Elicitation offer efficient alternatives to traditional descriptive analysis while maintaining connections to consumer relevance [41]. Through continued refinement of sensory panel methodologies and integration with complementary technologies, researchers can further decode the complex relationships between food structure, oral processing, and perceptual experience, ultimately enabling more precise design of food textures tailored to nutritional needs and consumer preferences.

Solving Texture Challenges in Formulation and Clinical Populations

The global push for healthier food products has made the reduction of sugar and fat a primary objective for the food industry. However, this endeavor is fraught with technical challenges, as these macronutrients play complex, multifunctional roles that extend far beyond mere sweetness and texture. Sugar contributes to sweetness, bitterness masking, flavor enhancement, viscosity, and bulk, while fat is a critical determinant of texture, mouthfeel, lubricity, flavor release, and aroma [43] [1]. Their removal or reduction creates significant formulation pitfalls, often resulting in products that fail to meet consumer expectations for sensory quality. This whitepaper examines the core challenges associated with sugar and fat reduction through the lens of food texture and mouthfeel research, providing a technical guide for scientists navigating this complex field. The central thesis is that overcoming these pitfalls requires a holistic, integrated approach that addresses the simultaneous loss of multiple sensory and functional properties.

The Sugar Reduction Challenge: Beyond Sweetness

The Multidimensional Role of Sugar in Foods

Sugar reduction is often mistakenly simplified to a matter of replacing sweetness. In reality, sugar's role is multidimensional. Scientifically, its removal triggers a cascade of sensory and physical deficits that must be systematically addressed [43].

• Sweetness and Temporal Profile: Sugar provides a rapid onset of sweetness and clean finish, a temporal profile that is difficult to replicate with many high-intensity sweeteners, which often exhibit delayed onset or lingering sweetness [43].
• Masking Properties: Sugar effectively masks undesirable off-tastes, particularly bitterness. Its removal can unveil bitter notes from other ingredients, including fortification nutrients or inherent compounds in the base food matrix [43].
• Flavor and Aroma Modulation: Sugar enhances flavor intensity and delivery, influencing the release and perception of volatile aroma compounds. Reduced-sugar formulations often suffer from flattened flavor profiles [43] [1].
• Mouthfeel and Body: As a bulking agent, sugar contributes to viscosity, thickness, and overall body perception in beverages and semi-solid foods. Its absence results in a thin, watery, or otherwise deficient mouthfeel [43].
• Preservation and Structure: Technologically, sugar contributes to preservation, water activity control, and structural integrity in products like baked goods, which are compromised in reduced-sugar versions [43].

Strategic Solutions and Sweetener Properties

Overcoming the sugar reduction challenge requires a multi-pronged strategy. No single solution can fully replicate sugar's functionality, necessitating combinatorial approaches.

Table 1: Key Challenges and Compensatory Strategies in Sugar Reduction

Challenge	Compensatory Strategies	Key Considerations
Sweetness Loss	High-intensity sweeteners (Artificial: Ace-K, Sucralose; Natural: Steviol glycosides, Monk fruit), Positive Allosteric Modulators (PAMs)	Temporal profile mismatch (onset/linger); consumer preference for "natural" labels [43].
Off-Taste Manifestation	Bitter-blockers, flavor modulators, blending sweeteners, flavors with modifying properties (FMPs)	Requires careful balancing to avoid introducing new off-notes or compromising target flavor [43].
Mouthfeel Loss	Hydrocolloids (gums, fibers), sugar alcohols (polyols), soluble fibers, mineral salts	Can alter viscosity and lubrication independently; may impact flavor release and gut tolerance [43].
Flavor Dilution	Potent flavor systems, flavor enhancers, spray-dried flavors, taste-triggered release technologies	Must restore flavor impact without making the product taste "artificial" or overpowering [43].

The Fat Reduction Challenge: Beyond Lubricity

The Critical Role of Fat in Mouthfeel and Flavor

Fat is a primary contributor to the desirable texture and mouthfeel of foods, and its reduction profoundly alters the sensory experience. The challenge is particularly acute because fat's functions are both physical and chemical in nature.

• Lubrication and Creaminess: Fat provides lubricity, which is directly perceived as smoothness and creaminess. This is a tribological property, relating to friction between oral surfaces [2] [1].
• Flavor Solvation and Release: Fat acts as a solvent for lipophilic flavor compounds, controlling their release during oral processing. Reduced-fat products often suffer from rapid "burst" release of flavors or a lack of sustained flavor, altering the overall perception [44] [1].
• Viscosity and Structure: In many emulsion-based products, fat globules contribute to thickness and structure. Fat crystals in solid products provide a specific melting profile that is central to sensory pleasure [44].
• Aroma and Mouth-Coating: The slow release of aromas from fat and the lingering mouth-coating sensation it provides are critical for a sustained and satisfying flavor experience [1].

Innovative Fat Replacement Systems

Advanced fat replacers are moving beyond simple hydrocolloid-based texturizers to sophisticated systems that mimic both the lubricating and flavor-carrying properties of fat.

Table 2: Analytical Methods for Evaluating Mouthfeel in Reduced-Sugar/Fat Products

Attribute	Instrumental Method	Measured Parameter(s)	Sensory Correlation
Thickness/ Viscosity	Rheology	Shear viscosity, yield stress, complex modulus (G*)	Perceived thickness, mouth-coating [2]
Smoothness/ Creaminess	Tribology	Coefficient of friction (μ) vs. speed (Stribeck curve)	Smoothness, creaminess, slipperiness [2]
Astringency	Tribology, QCM, E-Tongue	Friction increase, adsorption to salivary pellicle	Dry, puckering feeling [2] [1]
Graininess	Particle Size Analysis, Imaging	Particle size distribution, shape	Grittiness, powdery feel [2]

A prominent research direction involves switchable emulsion gels. For instance, a 2025 study successfully created a dual-network gel using gellan gum (thermo-reversible) and curdlan (thermo-irreversible). This system was filled with an emulsion to form a fat-mimicking gel. The gellan gum allows for flavor precursor mobility during heating, while the curdlan network maintains structural integrity at high temperatures, enabling controlled Maillard reaction and meat flavor generation upon cooking [44] [45]. This addresses a key pitfall of fat reduction: the loss of flavor development during thermal processing.

Advanced Experimental Methodologies for Formulation

Instrumental and Sensory Evaluation Protocols

Robust evaluation is critical for diagnosing and solving formulation problems. The following protocols represent state-of-the-art methodologies.

Protocol 1: Comprehensive Temporal Dominance of Sensations (TDS) Application: Dynamic sensory profiling of sweetener aftertaste or fat-replaced product breakdown. Method:

• Panel Training: Train a panel (n≥12) to recognize specific attributes (e.g., sweetness, bitterness, metallic, astringent, creamy, gritty).
• Software Setup: Use TDS software where assessors see all attributes on a screen.
• Task: For each evaluation, panelists select the dominant sensation they perceive at every moment from the first bite/sip until swallowing and the afterfeel.
• Data Collection: The software records the sequence and duration of dominant attributes.
• Analysis: Calculate dominance rates for each attribute over time. Plot TDS curves to visualize the temporal trajectory of the sensory experience, identifying problematic lingering notes or insufficient dominance of key attributes [2].

Protocol 2: Tribological Analysis of Lubricity (Mimicking Oral Conditions) Application: Quantifying the slickness and creaminess of fat-replaced systems. Method:

• Sample Preparation: Prepare samples under controlled conditions (temperature, shear history).
• Tribometer Setup: Use a soft tribometer with PDMS (polydimethylsiloxane) spheres as artificial tongue surfaces. Flood the contact with artificial saliva for biological relevance.
• Measurement: Perform a speed sweep (e.g., 1-500 mm/s) under a fixed load, measuring the coefficient of friction (COF) at each speed.
• Data Modeling: Plot the Stribeck curve (COF vs. speed). The curve's profile—particularly the boundary and mixed lubrication regimes—is highly correlated with sensory smoothness and creaminess. A lower COF in the mixed regime typically indicates a smoother, creamier product [2].

An Exemplar Experimental Workflow: Developing a Switchable Fat Replacer

The following diagram synthesizes the experimental workflow from a recent study on developing a texture and flavor-switchable emulsion gel, illustrating the integrated approach needed for successful fat replacement [44].

The Scientist's Toolkit: Key Reagents and Technologies

This section details essential research reagents and advanced technologies for addressing sugar and fat reduction pitfalls, as identified in the reviewed literature.

Table 3: Research Reagent Solutions for Reformulation Challenges

Category / Reagent	Function in Research	Key Consideration
Sweeteners
Steviol Glycosides	Natural high-intensity sweetness.	Can have bitterness/licorice off-notes at high concentrations; requires blending [43].
Monk Fruit Extract	Natural high-intensity sweetness.	Cleaner aftertaste than stevia but cost can be prohibitive [43].
Mouthfeel Enhancers
Gellan Gum	Forms thermo-reversible gels; provides texture and stability in emulsion gels.	Synergizes with other gums; type (high/low acyl) determines gel texture [44].
Curdlan	Forms thermo-irreversible, resilient gels upon heating.	Provides backbone structure in composite gels during high-temp processing [44].
Hydrocolloid Blends (e.g., Xanthan, Guar)	Control viscosity, yield stress, and suspension.	Can impart undesirable "slimy" mouthfeel if not correctly dosed and balanced [43].
Flavor Systems
d-ribose & L-cysteine	Maillard reaction precursors for generating savory/meaty flavors in situ during cooking.	Enables flavor generation in fat replacers that lack inherent flavor compounds [44].
Bitter Blockers (e.g., Homoeriodictyol sodium salt)	Suppress bitterness from peptides, minerals, or sweeteners.	Efficacy is system-dependent; can have its own off-taste at high levels [43].
Advanced Tools
Food Texture Analyzer	Quantifies mechanical properties (hardness, cohesiveness, adhesiveness).	Probes and fixtures must be selected to mimic oral processing (e.g., biting, chewing) [46].
Machine Learning Models (Autoencoders)	Predicts sensory texture from limited rheological and compositional data.	Reduces reliance on large-scale human panels in early-stage development [12].

The future of solving formulation pitfalls lies in the convergence of advanced material science, digital tools, and a deeper understanding of oral physiology. Machine learning (ML) models, such as the sensory-based autoencoder developed at Purdue University, are emerging as powerful tools to predict texture perception from quantitative physical properties, drastically accelerating the design cycle [12]. Furthermore, the integration of micro-analytical techniques, tactile sensing, and biomimetic oral models will provide a more fundamental and mechanistic understanding of how food structures disassemble in the mouth and generate specific mouthfeel perceptions [2].

In conclusion, overcoming the pitfalls of sugar and fat reduction is a complex, multidimensional problem that cannot be solved by simple substitution. Success requires a holistic strategy that simultaneously addresses sweetness, mouthfeel, flavor release, and temporal perception. This involves the intelligent application of novel ingredients like dual-function emulsion gels, the sophisticated use of instrumental techniques like tribology to quantify mouthfeel, and the adoption of cutting-edge digital tools like machine learning. By integrating these advanced technological approaches, food scientists can design reduced-sugar and reduced-fat products that truly deliver a satisfying sensory experience, meeting both consumer health demands and taste expectations.

Texture Modification for Dysphagia and Age-Related Swallowing Difficulties

Texture modification represents a foundational dietary intervention for the 10–33% of older adults globally affected by oropharyngeal dysphagia, a condition that significantly elevates the risk of malnutrition, aspiration pneumonia, and reduced quality of life [47]. Within the broader research context of food texture and mouthfeel, dysphagia management requires a precise engineering approach to food architecture. As established in mouthfeel research, texture is not merely an additive property but rather "the fundamental architecture of a food product, designed and built into the very structure of the ingredients and their interactions" [48]. This understanding is critical when developing foods for individuals with impaired swallowing function, where the dynamic textural journey from first bite to swallow must be carefully controlled to ensure safety and adequate nutritional intake.

The interplay between age-related physiological changes and textural perception further complicates this landscape. A systematic review on textural perception in older adults identified that oral manipulation capabilities, age-associated changes to the oral cavity, and psychological factors collectively drive food acceptance and intake in this population [49]. This underscores the necessity of texture-modified (TM) foods that address both physiological safety and hedonic acceptance. Current innovations in the field are increasingly leveraging multidisciplinary technologies—including additive manufacturing, artificial intelligence, and bionic devices—to create next-generation dysphagia foods that meet these complex requirements [47].

Standards and Classification Frameworks

The International Dysphagia Diet Standardisation Initiative (IDDSI) framework has emerged as the global standard for classifying texture-modified foods and thickened liquids, providing a common language for clinicians, researchers, and food manufacturers [50]. This standardized classification is crucial for ensuring patient safety and enabling reproducible research. The framework describes a continuum from Level 0 (thin liquids) to Level 7 (regular foods), with specific testing methods for each level, including fork drip tests, spoon tilt tests, and fork pressure tests [47].

The development of TM foods according to these standards requires precise characterization techniques. Rheology, which measures flow and deformation properties, and tribology, which studies friction and lubrication, are indispensable tools for quantifying the textural properties of dysphagia foods [47]. These instrumental measurements provide objective data that correlates with sensory perception and swallowing safety, allowing researchers to design foods with specific functional properties. For instance, a food classified as IDDSI Level 4 (pureed) must hold its shape on a spoon yet be easily swallowed with minimal chewing effort, requiring specific rheological parameters that can be consistently reproduced [50].

Key Textural Parameters and Measurement Methodologies

Fundamental Texture Properties

The design of effective dysphagia foods requires careful optimization of multiple textural properties, each measured through specific instrumental techniques. The following table summarizes the key properties and their measurement approaches:

Table 1: Key Textural Properties in Dysphagia Food Design

Textural Property	Functional Significance in Dysphagia	Common Measurement Techniques
Firmness/Hardness	Determines required chewing effort; excessive values may cause choking.	Texture Profile Analysis (TPA), penetration tests [50]
Cohesiveness	Affects bolus formation and integrity during swallowing.	Texture Profile Analysis (TPA) [50]
Adhesiveness	High values may cause food to stick to oral/pharyngeal surfaces.	Texture Profile Analysis (TPA) [51]
Springiness	Influences how food recovers from deformation during chewing.	Texture Profile Analysis (TPA)
Viscosity	Critical for liquid safety; thin liquids are higher aspiration risk.	Rheometry at shear rate 50 s⁻¹ [51]

Shape Retention and Spreadability

Beyond fundamental texture properties, shape retention has been identified as a particularly significant parameter for semi-solid foods commonly consumed by older adults [51]. Defined as the ability of a food to maintain its structure under gravity without additional force, shape retention represents a crucial intersection of solid and liquid properties in semi-solid matrices. The Line-Spread Test (LST) provides a simple yet effective methodology for quantifying this property by measuring the distance a standard amount of food spreads when released from a cylinder onto a flat surface [51]. Research utilizing neck-worn electronic stethoscopes (NWES) has demonstrated that variance in swallowing duration across different foods reveals important information about food-swallowing behavior compatibility, with shape retention being a key differentiating factor [51].

Experimental Approaches and Research Methodologies

Swallowing Assessment Protocols

Objective assessment of swallowing function is essential for evaluating the efficacy of texture-modified foods. Recent research has employed minimally invasive swallowing assessment methodologies that enable evaluation under near-normal eating conditions. The following workflow illustrates a comprehensive swallowing assessment protocol:

Diagram 1: Swallowing Assessment Workflow

A recent study provides a exemplary experimental protocol utilizing a neck-worn electronic stethoscope (NWES) for swallowing assessment [51]. The methodology proceeded as follows:

• Participants: Community-dwelling adults aged ≥75 years were recruited, excluding those with neurological conditions or pre-existing swallowing disorders [51].
• Food Samples: Three semi-solid foods (P, Q, S) with similar nutritional profiles but differing shape retention properties (LST values: P=19-20mm, Q=27mm, S=16mm) and comparable viscosities (approximately 1300-1700 mPa·s) were prepared [51].
• Instrumentation: A neck-worn electronic stethoscope was positioned on the neck to capture swallowing sounds, synchronized with video recording to visually confirm swallowing events [51].
• Data Collection: Participants ingested five consecutive bites of each test food in randomized order. The entire process from food intake to swallowing was recorded [51].
• Outcome Measures: Primary outcomes were oral processing time (OPT), defined as the time from food entrance into the oral cavity to the onset of swallowing, and swallowing duration (SD), measured from the onset to the completion of swallowing sounds [51].
• Statistical Analysis: Medians of five bites were calculated, and first-fifth bite differences were assessed using appropriate non-parametric tests [51].

This protocol successfully demonstrated that food properties significantly affect swallowing patterns in older adults, with OPT varying significantly between foods with different shape retention properties [51].

Texture-Modified Food Development Protocol

The development of texture-modified foods with optimized sensory properties requires systematic approaches. The following diagram illustrates a methodology for creating unified-texture dishes:

Diagram 2: Texture-Modified Food Development

A comprehensive study on developing texture-modified meat and thickened soup illustrates a sophisticated experimental approach [50]. The methodology encompassed three distinct stages:

• Stage 1: TM Meat Development: Two softening treatments were compared: (1) vacuum papain impregnation, and (2) grinding and reconstitution using transglutaminase (TGase) with added pea protein (0-1%), olive oil (0-10%), and papain (0-0.2%). The grinding and reconstitution method was selected as superior for maintaining solid appearance while achieving target texture [50].
• Stage 2: Soup Thickening: Commercial vegetable and chicken soup was thickened with guar gum at varying concentrations to match the firmness of the selected TM meat, ensuring uniform texture consistency between components [50].
• Stage 3: Dish Assembly and Testing: The TM meat and thickened soup were combined, classified according to IDDSI Level 4 standards, and subjected to comprehensive texture analysis to verify homogeneous texture properties throughout the dish [50].

This approach successfully demonstrated that solid and liquid components can be engineered to similar firmness levels, addressing both safety and visual appeal challenges in dysphagia diets [50].

Advanced Technologies and Innovative Approaches

Additive Manufacturing (3D/4D Printing)

Additive manufacturing, particularly 3D food printing, has emerged as the most transformative technology for dysphagia food production, enabling precise control over both external structure and internal texture [47]. This technology allows creation of visually appealing food structures that maintain safety requirements for dysphagia patients. Recent advancements include 4D printing techniques that enable dynamic changes in food properties post-printing; for example, using pH-responsive anthocyanins from purple sweet potato to create color-changing, high-protein dysphagia foods [47].

The application of 3D printing to various food matrices has been extensively demonstrated. For instance, research has successfully utilized chicken surimi incorporated with mealworm protein isolate to create coaxial 3D printed structures suitable for elderly consumers with chewing difficulties [47]. Similarly, protein gels with designed network structures have been developed using 3D printing to achieve specific textural properties aligned with dysphagia diet classifications [47].

Intelligent Technologies and Computational Modeling

The integration of artificial intelligence with swallowing visualization techniques and wearable sensors represents a frontier in dysphagia food research [47]. These technologies enable precise analysis of physiological signals during swallowing, providing data-driven feedback for food design. Complementing these approaches, bionic devices that simulate swallowing in vitro offer opportunities to characterize textural changes of dysphagia foods throughout the swallowing process without human subject testing [47].

Mathematical modeling has also emerged as a powerful tool for understanding swallowing mechanics. Researchers at Kyushu University have developed a computational model that recreates esophageal muscle movements during swallowing, simulating both normal peristalsis and various motility disorders by adjusting parameters such as nerve signal strength and muscle contraction intensity [52]. This model provides theoretical insights into the underlying causes of swallowing disorders and offers potential avenues for treatment development [52].

Similarly, collaborative research between NIST and Northwestern University has produced computer models of the esophagus to study conditions like corkscrew esophagus, examining how pressure variations during swallowing contribute to tissue damage and abnormal morphologies [53]. These models allow non-invasive measurement of esophageal pressure and identification of thresholds that may lead to pathological conditions [53].

Research Reagents and Materials Toolkit

The following table catalogues essential research reagents and materials employed in the development and analysis of texture-modified foods for dysphagia, as referenced in the cited studies:

Table 2: Essential Research Reagents for Dysphagia Food Development

Reagent/Material	Function/Application	Example Use Case
Guar Gum	Thickening agent for liquids and purees	Creating thickened soups with specific firmness levels matched to solid components [50]
Papain	Proteolytic enzyme for meat tenderization	Softening meat texture while maintaining solid appearance via vacuum impregnation or grinding/reconstitution [50]
Transglutaminase (TGase)	Enzyme that catalyzes protein cross-linking	Reconstituting ground meat into solid forms with customized texture properties [50]
Pea Protein	High-quality plant protein source	Nutritional fortification of texture-modified foods to address common protein deficiencies [50]
Hydroxypropyl Methylcellulose (HPMC)	Hydrocolloid for texture modification and 3D printing	Creating viscoelastic food inks for additive manufacturing of dysphagia foods [47]
Konjac Glucomannan	Gelling and thickening agent	Forming protein gels with designed network structures for dysphagia diets [47]
Rheometer	Instrument for measuring viscosity and flow properties	Quantifying viscosity at standardized shear rates (e.g., 50 s⁻¹) for liquid characterization [51]
Neck-Worn Electronic Stethoscope (NWES)	Device for capturing swallowing sounds	Non-invasive assessment of swallowing duration and patterns during food consumption [51]

The field of texture modification for dysphagia and age-related swallowing difficulties is undergoing rapid transformation, driven by interdisciplinary approaches that bridge food science, engineering, and clinical medicine. The continued development and refinement of standardized classification systems like IDDSI provides a crucial foundation for reproducible research and clinical practice. Meanwhile, emerging technologies—including additive manufacturing, intelligent sensors, and computational modeling—offer unprecedented capabilities for creating personalized, safe, and appealing dysphagia foods that address both physiological needs and hedonic acceptance.

Future research directions will likely focus on further personalization of texture-modified foods based on individual swallowing capabilities and nutritional requirements. The integration of real-time swallowing assessment with rapid food customization technologies represents a particularly promising avenue. Additionally, continued refinement of mathematical models of swallowing mechanics may enable virtual screening of food textures for specific patient populations, accelerating the development process. As these advancements mature, they hold significant potential for improving nutritional status, reducing aspiration risk, and enhancing quality of life for the growing global population affected by swallowing difficulties.

The Influence of Oral Physiology Changes in Older Adults on Food Acceptance

Food acceptance in older adults is a critical determinant of nutritional status and overall health. This in-depth technical guide examines the influence of age-related oral physiological changes on food acceptance, framed within the broader context of food texture and mouthfeel research. As global populations age rapidly, with those aged ≥65 years projected to constitute 16% of the world's population by 2050, understanding these dynamics becomes increasingly crucial for researchers, scientists, and product development professionals [54]. The complex interplay between oral physiology, textural perception, and food intake behaviors creates significant challenges for maintaining adequate nutrition in aging populations, with malnutrition prevalence reaching as high as 50% in elderly care facilities [55]. This whitepaper synthesizes current research on oral processing mechanisms, physiological changes, and methodological approaches to provide a comprehensive scientific framework for addressing these challenges through targeted interventions and product design strategies.

Oral Physiological Changes in Aging

Age-Associated Structural and Functional Declines

The oral processing system undergoes significant transformations with advancing age, impacting all stages of food breakdown and perception. These changes encompass multiple physiological domains, including muscular, dental, salivary, and sensory functions, collectively altering the eating experience and nutritional outcomes for older adults.

Table 1: Key Oral Physiological Changes in Older Adults and Their Impact on Food Processing

Physiological Domain	Specific Age-Related Changes	Impact on Food Oral Processing
Dentition	Tooth loss, weakened mastication muscles, reduced bite force	Reduced ability to fracture hard foods, limited particle size reduction, prolonged chewing duration
Salivary Function	Reduced salivary flow (hyposalivation), altered composition	Impaired bolus formation, delayed swallowing, reduced solubilization of tastants
Oral Motor Skills	Decreased tongue strength and coordination, slowed oral movements	Impaired food manipulation, transportation, and clearance, increased residue
Sensory Perception	Reduced taste and smell sensitivity, higher perception thresholds	Diminished flavor perception, altered seasoning preferences, reduced eating pleasure
Swallowing Function	Delayed swallowing reflex, reduced pharyngeal sensitivity	Increased risk of aspiration, need for safer swallowing strategies

The deterioration of masticatory performance, frequently exacerbated by loss of dentition, significantly impacts the preparatory stages of swallowing [55]. This is compounded by salivary dysfunction, which plays a crucial role in bolus formation, lubrication, and the initial chemical breakdown of food. The resulting compromised safety and efficiency of swallowing, clinically referred to as dysphagia, creates a physiological barrier to adequate nutrition and introduces psychological aversions to previously enjoyed foods [49] [55].

Consequences for Nutritional Status and Health

The cumulative effect of these physiological changes establishes a "vicious cycle" of nutritional decline. As eating becomes more difficult and less pleasurable, food intake decreases, leading to nutritional deficiencies that further exacerbate physical decline and reduce appetite [55]. This cycle particularly impacts protein and micronutrient intake, as the need for these essential nutrients remains unchanged despite overall reductions in food consumption [54]. Meal skipping emerges as a significant contributor to malnutrition in this population, disrupting regular eating patterns and reducing both overall food intake and diet quality [54]. The resulting nutritional deficits increase morbidity and mortality risks, emphasizing the critical importance of addressing oral physiological barriers to food acceptance.

Texture Perception and Food Acceptance Mechanisms

The Role of Mouthfeel in Food Preference

Mouthfeel represents a complex, multidimensional sensory experience that integrates physical, tactile, and textural sensations in the oral cavity [1]. While often used interchangeably with texture, mouthfeel encompasses broader sensory dimensions, including thermal sensations, astringency, and chemical irritancy, mediated primarily through the trigeminal nerve [1]. For older adults with diminished taste and olfactory function, textural attributes frequently become the dominant factor in food acceptance and rejection decisions.

The perception of texture is not merely a passive registration of food properties but an active process involving continuous sensory feedback during oral processing. This dynamic perception significantly drives product acceptance and intake in older populations [49]. Research indicates that undesirable texture serves as a key driver of food aversion, while positively perceived textural attributes can enhance eating pleasure even when flavor perception is compromised [1].

Interplay Between Oral Physiology and Texture Perception

The relationship between oral physiological status and texture perception operates through multiple mechanisms. During consumption, food structures undergo progressive breakdown through mechanical and chemical processes (interaction with saliva), releasing both volatile and non-volatile compounds [1]. Non-volatile compounds, including tastants and texture-related molecules, activate gustatory and trigeminal systems, creating the integrated experience of mouthfeel [1].

Age-related changes in oral anatomy directly impact this process by altering the biomechanics of food breakdown. For instance, reduced oral volume and morphological changes in the palatal arch affect food manipulation, while mucosal thinning can modify thermal sensitivity and textural discrimination [49]. Furthermore, salivary composition changes influence lubrication efficiency and the perceived smoothness or astringency of foods [1]. These physiological alterations create a fundamentally different oral processing environment in older adults compared to younger populations, necessitating specialized food design approaches.

Research Methodologies and Experimental Protocols

In Vivo Assessment Techniques

Understanding oral processing in older adults requires multidisciplinary approaches that capture the dynamic nature of eating. In vivo methodologies provide direct insight into physiological processes and perceptual responses during food consumption.

Table 2: In Vivo Methodologies for Assessing Oral Processing in Older Adults

Methodology	Measured Parameters	Application in Older Adults	Considerations
Electromyography (EMG)	Muscle activity patterns, duration and amplitude of bursts, chewing efficiency	Assess masticatory performance, muscle fatigue, adaptation strategies	Non-invasive surface electrodes preferred; cannot provide absolute force measurements [55]
Jaw Movement Kinematics (KJM)	Chewing cycle duration, jaw trajectory, velocity, and rhythm	Evaluate oral motor control, coordination deficits, swallowing triggers	Skin surface markers less intrusive than tooth-attached transducers [55]
Video Jaw Tracking	Simple 2D tracking of jaw movement using facial stickers	Basic oral processing parameters in vulnerable populations	Fast setup, suitable for children or dysphagic patients [55]
Sensory Evaluation Panels	Perceived intensity of textural attributes, liking scores, temporal dominance of sensations	Direct measurement of texture perception and preference	Require careful protocol design to minimize context effects; resource-intensive [1] [56]

Electromyography and jaw movement tracking have revealed that older adults employ adaptive chewing strategies, including increased chewing cycles, reduced chewing frequency, and altered muscle recruitment patterns to compensate for physiological limitations [55]. These adaptations significantly influence texture perception by extending oral processing time and modifying mechanical forces applied to food structures.

Sensory Evaluation Protocols

Sensory testing with older populations requires specialized protocols that account for potential cognitive and sensory limitations. The sucrose-sweetener combined method has been identified as particularly effective for modeling concentration-response relationships in complex food systems, as it mitigates context effects that can distort intensity ratings [56]. This approach involves presenting both the target sweetener and sucrose reference standards within the same test set, enabling more accurate sensory comparisons.

Key considerations for sensory testing with older adults include:

• Session duration: Shorter sessions to prevent fatigue
• Sample size: Smaller portions accounting for reduced appetite
• Anchor references: Frequent use of clear reference standards to combat sensory memory decline
• Scale design: Simplified scaling methods accommodating potential cognitive constraints
• Context control: Careful sample presentation order to minimize contrast and convergence effects

Instrumental and In Silico Approaches

While in vivo methods provide essential physiological and perceptual data, they present practical challenges for routine industry application, including substantial recruitment, training, and scheduling requirements [55]. Consequently, complementary in vitro and in silico approaches have gained prominence for food design applications.

Rheological characterization, particularly extending into the non-linear domain, provides fundamental insights into mechanical properties relevant to texture perception [55]. Tribological approaches examining lubricative properties have shown correlations with sensory attributes like smoothness and creaminess, though limitations exist in directly translating friction factors to perceptual roughness [55]. Emerging methodologies include electronic tongues for taste approximation and quartz-crystal microbalance (QCM) sensors for adsorption studies [1].

Machine learning represents a transformative approach to predicting texture perception. Recent research demonstrates that artificial intelligence models, specifically sensory-based autoencoders, can accurately predict mouthfeel based on measurable physical properties of food, even with limited datasets [12]. These models use cross-validation techniques to ensure reliability from small sample sizes, enabling reverse-engineering of foods tailored to specific physiological needs [12].

Figure 1: Relationship between oral physiological changes, food processing, and nutritional consequences in older adults

The Scientist's Toolkit: Research Reagent Solutions

Essential Materials for Oral Processing Research

Table 3: Key Research Reagents and Materials for Studying Oral Processing in Older Adults

Item/Category	Function/Application	Specific Examples/Parameters
Oral Nutritional Supplements (ONS)	Nutritional intervention; meal replacement for skipped meals	Miniwell OS (200 kcal, 9g protein per 150mL); provides 400 kcal, 18g protein daily as meal replacement [54]
Texture-Modified Foods	Study of swallowing safety and acceptance in dysphagia	Viscosity-modified liquids (nectar-, honey-, pudding-thick); mechanically softened foods [55]
Electromyography Systems	Monitoring muscle activity during mastication	Surface electrodes for masseter and temporalis muscles; measures activity patterns and relative recruitment [55]
Jaw Tracking Systems	Kinematic analysis of chewing patterns	Skin surface markers or video-based tracking; measures chewing cycle duration, jaw trajectory [55]
Rheological Instruments	Quantification of mechanical food properties	Controlled stress rheometers; measures viscosity, yield stress, viscoelastic properties [55] [1]
Tribological Equipment	Assessment of lubricative properties	Tribometers with simulated oral surfaces; measures friction factors related to smoothness perception [55]
Sensory References	Calibration of sensory panels for texture evaluation	Standardized thickness series (water to pudding); particle size standards; astringency references (tannin solutions) [56]

Assessment Tools and Nutritional Interventions

Nutritional assessment tools form a critical component of geriatric nutrition research. The Mini Nutritional Assessment (MNA) provides validated evaluation of nutritional status through 18 items related to weight loss, appetite, and general health, with scores <17 indicating malnutrition [54]. The Nutrition Quotient for the Elderly (NQ-E) specifically assesses dietary quality across domains including balance, moderation, and dietary practices [54].

For physical function assessment, the Short Physical Performance Battery (SPPB) evaluates balance, gait speed, and chair-stand performance, providing crucial functional correlates to nutritional status [54]. Bioelectrical impedance analyzers (e.g., Inbody 970) enable detailed body composition analysis, tracking changes in lean body mass, fat mass, and body fat percentage in response to nutritional interventions [54].

Implications for Product Development and Future Research

Designing Age-Appropriate Food Products

Understanding the oral capabilities and sensory-specific needs of older adults enables targeted food product development. The heterogenous nature of this population necessitates segment-specific approaches that account for varying degrees of physiological impairment [49]. Key strategies include:

Texture Modification: Optimizing mechanical properties to match oral capabilities while maintaining eating pleasure. This includes softening foods for easier breakdown while preserving textural interest, and modulating viscosity for safer swallowing without compromising flavor release [55].

Nutrient Density Enhancement: Fortifying foods with essential proteins, vitamins, and minerals to compensate for reduced consumption volumes, addressing the imbalance between decreased food intake and maintained nutrient requirements [54].

Flavor Amplification: Compensating for sensory declines through targeted seasoning strategies that enhance perceived flavor without exceeding sodium or sugar recommendations. This includes leveraging aroma release modulation and cross-modal interactions to enhance sweetness or saltiness perception without increasing actual content [55] [1].

Emerging Technologies and Research Directions

The field of food oral processing research continues to evolve with several promising directions:

Machine Learning Applications: AI models for texture prediction represent a paradigm shift in food design, potentially reducing reliance on costly human testing while enabling precise tailoring of textural properties to specific physiological needs [12].

Personalized Nutrition Approaches: Moving beyond population averages to develop individualized food solutions based on comprehensive oral health and sensory capability assessments [55].

Advanced Biomimetic Models: Developing more sophisticated in vitro and in silico models that better simulate the complex oral environment, including salivary components, mucosal surfaces, and dynamic processing conditions [55].

Multi-sensory Integration Studies: Deeper investigation of how texture perception interacts with other sensory modalities in the aging consumer to develop more satisfying eating experiences despite sensory impairments [1].

As research in this field advances, the integration of physiological understanding with technological innovation holds significant promise for enhancing food acceptance, nutritional intake, and quality of life for the growing global population of older adults.

Optimizing Texture for Patient Adherence and Nutritional Intake

Within the broader thesis of food texture and mouthfeel research, the clinical setting presents a unique and high-stakes environment. Texture is not merely a matter of consumer preference but a critical factor influencing patient safety, nutritional status, and clinical outcomes. Mouthfeel, defined as the complex sensory perception involving physical, tactile, and textural sensations experienced in the mouth, is a multidimensional aspect of flavor perception [1]. In hospital patients, particularly those with dysphagia, chewing difficulties, or reduced appetite, the mechanical and structural properties of food—its texture—can determine whether nutritional requirements are met [57]. When texture is undesirable, it becomes a key driver of food aversion and rejection, directly impacting dietary intake and potentially contributing to disease-related malnutrition [1]. This technical guide synthesizes current research and methodologies to provide a framework for optimizing texture-modified diets (TMDs) to enhance patient adherence and nutritional intake.

Clinical Evidence: The Impact of Texture Modification on Nutritional Intake

Quantitative Evidence from Observational Studies

A recent observational study conducted at the Fondazione Policlinico Universitario Agostino Gemelli IRCCS provides critical quantitative data on the relationship between diet texture and nutritional intake in hospitalized patients. The study evaluated dietary intake across three diet types, revealing significant nutritional challenges [57].

Table 1: Nutritional Intake Across Texture-Modified and Standard Diets During Lunch Meal

Diet Type	IDDSI Level	Average Caloric Intake (kcal)	Average Protein Intake (g)	Patients Not Meeting Minimum Requirements
Soft Diet	Level 4 (Pureed)	473.4 (across all diets)	30.9 (across all diets)	>40% (across all diets)
Minced Diet	Level 6 (Soft & Bite-Sized)	473.4 (across all diets)	30.9 (across all diets)	>40% (across all diets)
Standard Diet	N/A	473.4 (across all diets)	30.9 (across all diets)	>40% (across all diets)

Note: Minimum nutritional requirement for lunch was set at 513 kcal and 30g of protein, representing 30% of daily needs [57].

The data demonstrates that regardless of diet texture, a substantial proportion of patients failed to meet minimal nutritional requirements during their lunch meal. This finding highlights a systemic challenge in clinical nutrition that extends beyond texture modification alone. Correlation analysis further identified significant positive associations between dietary intake and breakfast palatability for all diets, and between intake and breakfast quantity for standard and soft diets (ORs > 2, p < 0.05) [57]. This suggests that optimizing early-day meal acceptance may have cascading benefits throughout the day.

Key Factors Influencing TMD Acceptance

The clinical evidence points to several interconnected factors that influence adherence to texture-modified diets:

• Palatability and Meal Quality: Patient satisfaction with food appearance, taste, texture, and temperature significantly affects dietary intake rates [57].
• Mealtime Assistance: Patients requiring feeding assistance due to physical or cognitive limitations face additional barriers to adequate intake [57].
• Sensory-Specific Satisfaction: Texture modification often alters the fundamental sensory characteristics of foods, potentially reducing familiar mouthfeel attributes that contribute to eating pleasure [1].

Theoretical Framework: The Physiology of Mouthfeel Perception

Understanding how texture optimization improves adherence requires examining the physiological mechanisms of mouthfeel perception. The experience of mouthfeel involves complex integration across multiple sensory pathways.

Integrated Sensory Pathway of Mouthfeel Perception

The following diagram illustrates the physiological process of mouthfeel perception from food consumption to conscious experience:

Mouthfeel Perception Pathway

Key Physiological Systems in Mouthfeel

The trigeminal system, which mediates tactile, thermal, and chemical irritant sensations in the oral cavity, plays a particularly crucial role in mouthfeel perception [1]. This system detects attributes such as:

• Astringency: The drying, roughing sensation caused by certain compounds
• Cooling/Warming: Thermal sensations from compounds like menthol
• Pungency/Spiciness: Irritant sensations from various chemical stimuli

Simultaneously, saliva composition and flow rate critically influence flavor perception and mouthfeel by acting as a medium for dissolving flavor compounds and modulating textural sensations [1]. Its properties affect how attributes like smoothness, astringency, and coating are perceived.

Emerging Research and Innovation: Machine Learning in Texture Prediction

AI-Driven Texture Optimization Framework

Cutting-edge research from Purdue University's Department of Food Science demonstrates how artificial intelligence can predict texture perception based on measurable physical properties of food [12]. This approach addresses the fundamental challenge in food development: the subjective nature of human sensory evaluation.

The following workflow illustrates the machine learning approach to texture prediction:

ML Texture Prediction Workflow

This AI model, specifically a sensory-based autoencoder, can work with very small sample sizes—a significant advantage in clinical settings where large-scale sensory testing is often impractical [12]. The system was trained using data from just a few bouillon samples yet achieved reliable predictive capability through rigorous cross-validation techniques [12].

Applications for Clinical Populations

This technology enables reverse-engineering of foods tailored to patients with specific needs. For vulnerable populations such as the elderly or stroke patients with swallowing difficulties, texture is not merely about preference but safety—too thin consistencies can cause aspiration, while overly thick consistencies may be difficult to consume [12]. Machine learning models allow developers to precisely target optimal texture parameters that balance safety with palatability.

Experimental Protocols and Methodologies

Sensory Evaluation Protocol for TMD Development

Comprehensive sensory evaluation is essential for developing effective texture-modified diets. The following protocol provides a standardized methodology:

Objective: To quantitatively assess sensory perception and acceptability of texture-modified foods in target populations.

Materials:

• Test food samples with systematically varied texture parameters
• Visual analog scales (digital or paper-based)
• Standardized serving containers and utensils
• Water for palate cleansing
• Controlled testing environment

Procedure:

• Recruitment: Recruit representative participants (e.g., patients with specific conditions, elderly individuals) with appropriate ethical approval and informed consent.
• Sample Preparation: Prepare identical food samples varying only in the specific texture parameter(s) of interest.
• Serving: Serve samples in randomized order to control for sequence effects.
• Evaluation: Participants rate each sample using structured scales for:
  • Perceived thickness/viscosity
  • Ease of swallowing
  • Mouthcoating characteristics
  • Overall acceptability
  • Specific texture attributes (grittiness, smoothness, etc.)
• Data Analysis: Analyze results using appropriate statistical methods (ANOVA, multivariate analysis) to identify significant relationships between texture parameters and sensory responses.

Validation: Correlate sensory results with instrumental texture measurements where possible to establish predictive models.

Nutritional Intake Assessment Protocol

To evaluate the effectiveness of texture-optimized diets in clinical settings, the following observational protocol can be implemented:

Objective: To quantitatively measure actual consumption of texture-modified diets in hospitalized patients.

Materials:

• Standardized meal portions with known nutritional composition
• Visual estimation guide (5-point scale: 0%, 25%, 50%, 75%, 100%)
• Digital data collection system

Procedure:

• Training: Train clinical dietitians using photographic examples and sample trays to ensure consistency and minimize inter-rater variability [57].
• Observation: During lunch service, observe and record food intake for each meal component using the 5-point scale.
• Conversion: Convert intake percentages to estimated caloric and protein values using nutritional reference data for each meal component [57].
• Data Collection: Collect additional contextual data including:
  • Patient factors (appetite, need for assistance, clinical condition)
  • Meal quality perceptions (palatability, temperature, appearance)
  • Environmental factors (meal interruptions, timing)
• Analysis: Calculate nutritional adequacy by comparing actual intake to predetermined requirements (e.g., 513 kcal and 30g protein for lunch) [57].

The Scientist's Toolkit: Essential Reagents and Materials

Table 2: Key Research Reagent Solutions for Texture and Mouthfeel Research

Reagent/Material	Function in Research	Application Examples
Natural Thickeners (e.g., starches, gums)	Modify viscosity and mouthfeel without significantly altering flavor	Creating graded viscosity levels for dysphagia diets; mimicking creamy mouthfeel in plant-based products [29]
Gelling Agents (e.g., gelatin, pectin, agar)	Provide structure and form to texture-modified foods	Creating molded purees that maintain shape but require minimal chewing; developing heat-stable textures [29]
Plant-Based Proteins	Base for creating protein-rich textured foods	Developing meat analogues with fibrous textures; adding nutritional boost to texture-modified foods [29]
Rheology Instruments	Quantify mechanical properties and flow behavior of foods	Establishing correlation between instrumental measurements and sensory perception; quality control [1]
Tribometers	Measure friction and lubrication properties	Predicting mouthcoating and smoothness attributes; studying astringency mechanisms [1]

Future Directions and Trends in Texture Optimization

Emerging mouthfeel trends from food science research offer promising directions for clinical nutrition innovation:

• Multi-Layered Mouthfeel: Combining textures within a single food product (e.g., crunchy with soft) can create more engaging eating experiences for patients with limited diet variety [29].
• Mouthfeel Mimicry: Advanced formulation techniques using plant proteins, fats, and stabilizers can recreate the creamy richness of dairy or the tender juiciness of meat in modified-texture foods [29].
• Climate-Proof Formulations: Developing texture-stable products that resist environmental challenges such as temperature fluctuations during transport and storage [29].
• Blissfully Boosted Foods: Integrating functional ingredients (probiotics, botanicals, enhanced proteins) into familiar textures to support wellness goals without compromising eating pleasure [29].

Optimizing texture for patient adherence and nutritional intake represents a critical intersection of food science, clinical nutrition, and sensory research. The evidence clearly demonstrates that texture modification alone is insufficient without careful attention to palatability, meal quality, and individual patient factors. Emerging technologies, particularly machine learning approaches to texture prediction, offer powerful new tools for developing patient-specific solutions that balance safety with sensory appeal. By applying the methodologies, frameworks, and experimental protocols outlined in this technical guide, researchers and clinicians can advance the systematic optimization of texture-modified diets to improve nutritional outcomes and patient quality of life.

Correlating Data and Future Trends: From Machine Learning to Clinical Validation

In food texture and mouthfeel research, a significant challenge persists: the precise correlation between quantitative instrumental measurements and subjective human sensory perception. Mouthfeel, the tactile experience of texture and sensation in the mouth, is a multidimensional attribute crucial to consumer acceptance and product quality [1]. Despite advancements in food science, establishing predictive relationships between instrument-derived data and sensory panel evaluations remains complex due to the multifaceted nature of oral processing and individual physiological differences.

This technical guide examines current methodologies, instrumental techniques, and statistical approaches for creating robust correlations between instrumental and sensory data. By integrating insights from rheology, tribology, and dynamic sensory evaluation techniques, researchers can develop more accurate predictive models that bridge the gap between laboratory measurements and human experience, ultimately accelerating product development and quality control in food and pharmaceutical industries.

Theoretical Foundations of Texture Perception

Defining Texture and Mouthfeel

Food texture encompasses "all the mechanical, geometrical and surface attributes of a product perceptible utilizing mechanical, tactile and, where appropriate, visual and auditory receptors" according to ISO 11036:2020 [2]. These attributes are categorized into three distinct classes:

• Mechanical properties: Related to stress and strain response, including primary properties (hardness, cohesion, viscosity, elasticity, adhesion) and secondary properties (brittleness, chewiness, adhesiveness)
• Geometrical properties: Determined by particle size, shape, and orientation (e.g., grittiness, porosity)
• Surface properties: Characteristics of food surface influenced by moisture and fat content (e.g., moistness, creaminess) [2]

Mouthfeel differs from texture in that it represents the integrated sensory perception resulting from these physical properties during oral processing, making it more challenging to quantify instrumentally [1].

Physiological Mechanisms of Perception

The perception of mouthfeel involves multiple sensory systems working in concert:

• Somatosensory system: Detects tactile sensations through mechanoreceptors
• Trigeminal nerve: Responds to chemical irritants and temperature variations
• Gustatory system: Contributes to taste perception that interacts with textural attributes
• Salivary components: Influence lubrication and dissolution of food compounds [1]

During oral processing, food undergoes progressive structural breakdown through mastication, mixing with saliva, and temperature adaptation, creating a dynamic sensory experience that evolves over time [2]. This temporal dimension adds complexity to correlating instantaneous instrumental measurements with changing sensory perceptions.

Instrumental Methods for Texture Analysis

Fundamental Measurement Techniques

Instrumental methods for texture evaluation fall into three primary categories: fundamental, empirical, and imitative approaches [2].

Table 1: Fundamental Instrumental Methods for Texture Analysis

Method Category	Measurement Principle	Key Parameters	Common Applications
Rheology	Response to deformation forces	Viscosity, elasticity, viscoelastic moduli	Thickness evaluation of liquids, semisolids
Tribology	Friction and lubrication properties	Coefficient of friction, lubricating properties	Creaminess, smoothness, astringency
Acoustic Analysis	Sound emission during fracture	Acoustic frequency, amplitude, breakage patterns	Crispiness, crunchiness of brittle foods
Texture Profile Analysis (TPA)	Compression-extrusion cycles	Hardness, cohesiveness, springiness, chewiness	Gelatin gels, cheese, baked goods

Rheological methods excel at characterizing mechanical properties related to thickness perception, with viscosity measurements showing strong correlations with sensory thickness ratings for fluid foods [2]. Tribological approaches, which study friction between surfaces in relative motion, better capture surface-related attributes like smoothness and creaminess, particularly in the mixed and boundary lubrication regimes that mimic oral conditions [2].

Emerging Analytical Technologies

Novel analytical techniques are addressing previous limitations in geometrical property assessment:

• Micro-analytical techniques: Enable characterization of particle size and distribution directly relevant to graininess perception
• Tactile sensing systems: Biomimetic approaches that replicate human tactile responses
• Multi-modal sensing platforms: Integrated systems combining rheological, tribological, and acoustic measurements [2]

Advanced sensor technologies including electronic tongues (E-tongue), electronic noses (E-nose), and electronic eyes (E-eye) provide objective assessment of taste, aroma, and visual properties that interact with texture perception [58]. These tools offer rapid, non-invasive, and reproducible assessments that complement traditional sensory evaluation.

Sensory Evaluation Methods

Classical Descriptive Techniques

Sensory science employs rigorously controlled methodologies to quantify subjective perceptions:

• Quantitative Descriptive Analysis (QDA): Trained panels identify and quantify sensory attributes using standardized intensity scales, generating statistical data on product profiles [59]
• Free Choice Profiling (FCP): Assessors develop individual descriptive terms without extensive training, with data analyzed through Generalized Procrustes Analysis [59]
• Flash Profiling: Rapid comparative method based on attribute ranking without intensity measurement, effective for product positioning [59]

Dynamic Temporal Methods

Time-dependent sensory methods capture the evolving nature of mouthfeel perception:

• Time-Intensity (TI): Evaluators continuously score specific attributes along a time axis, mapping temporal changes in perception [2] [59]
• Temporal Dominance of Sensations (TDS): Panelists identify the dominant sensory attribute at each moment, revealing shifting perception hierarchies [2]
• Temporal Check-All-That-Apply (TCATA): Assessors select all applicable descriptive terms over time, enabling multidimensional analysis of sensory transitions [2]

These dynamic approaches better reflect the complex oral processing sequence where food structure continuously changes, providing more nuanced data for correlation with instrumental measurements.

Correlation Methodologies and Statistical Approaches

Experimental Design for Correlation Studies

Establishing meaningful correlations requires carefully controlled experimental designs that address both instrumental and sensory variables simultaneously. Key considerations include:

• Sample selection: Systematic variation of specific textural properties while controlling others
• Panel training: Extensive calibration using reference materials with established textural properties
• Measurement synchronization: Temporal alignment of instrumental and sensory data collection
• Replication: Sufficient repetitions to account for biological and instrumental variability

A structured workflow ensures data compatibility and facilitates robust statistical modeling.

Data Correlation Workflow

Multivariate Statistical Techniques

Advanced statistical methods are essential for deciphering complex relationships between instrumental and sensory datasets:

• Principal Component Analysis (PCA): Reduces dimensionality while preserving data structure, identifying underlying patterns
• Partial Least Squares Regression (PLSR): Maximizes covariance between predictor (instrumental) and response (sensory) variables
• Multiple Linear Regression (MLR): Models linear relationships between multiple instrumental parameters and sensory attributes
• Canonical Correlation Analysis (CCA): Identifies relationships between two sets of variables

These techniques handle the multicollinearity often present in instrumental measurements while extracting the most relevant predictors for sensory attributes.

Table 2: Correlation Strengths Between Instrumental Measurements and Sensory Attributes

Sensory Attribute	Best Instrumental Predictor	Correlation Strength (R²)	Statistical Method	Key Challenges
Thickness	Rheological viscosity	0.75-0.95	PLSR	Shear rate dependence
Creaminess	Tribological friction + Rheological viscosity	0.65-0.85	Multiple Regression	Multimodal integration
Astringency	Tribological friction + QCM adsorption	0.60-0.80	PLSR	Saliva interaction variability
Graininess	Particle size distribution	0.55-0.75	Multiple Regression	Threshold detection limits
Crunchiness	Acoustic emission + Fracture force	0.70-0.90	PCA + Regression	Rate dependence

Experimental Protocols for Key Correlations

Protocol for Thickness/Viscosity Correlation

Objective: Establish predictive relationship between instrumental viscosity measurements and sensory thickness perception.

Materials:

• Test samples with controlled viscosity variation (0.01-10 Pa·s)
• Controlled stress/rate rheometer with concentric cylinder or cone-plate geometry
• Trained sensory panel (n≥10)
• Reference standards with defined viscosity

Procedure:

• Instrumental Measurement:
  • Conduct flow curve analysis over shear rate range 0.1-100 s⁻¹
  • Record viscosity at shear rates 10 s⁻¹ and 50 s⁻¹ (approximating oral processing)
  • Perform oscillatory measurements (amplitude sweep) to determine linear viscoelastic region
  • Calculate zero-shear viscosity through Cross or Carreau model fitting

• Sensory Evaluation:

  • Implement balanced reference duo-trio test for sample orientation
  • Apply Quantitative Descriptive Analysis with 15-point intensity scales
  • Use calibrated references: water=1, 5% gum solution=5, 10% gum solution=10, 15% gum solution=15
  • Maintain sample temperature at 25°C±1°C
  • Implement mandatory 60-second rinse between samples

• Data Analysis:

  • Perform PLSR with viscosity parameters as X-matrix and thickness scores as Y-matrix
  • Validate model through full cross-validation
  • Establish prediction uncertainty through bootstrap resampling

Protocol for Creaminess Correlation

Objective: Develop multimodal model predicting sensory creaminess from instrumental parameters.

Materials:

• Emulsion systems with varying fat content (0-20%) and droplet size
• Rheometer with tribological attachment or separate tribometer
• Trained sensory panel (n≥12)
• Saliva collection apparatus

Procedure:

• Instrumental Measurement:
  • Rheological: Measure apparent viscosity at 50 s⁻¹, yield stress, and viscoelastic moduli
  • Tribological: Obtain Stribeck curves (0.1-1000 mm/s) using polydimethylsiloxane (PDMS) surfaces
  • Extract friction coefficients at entrainment speeds 10 mm/s (boundary regime) and 300 mm/s (mixed regime)
  • Microstructural: Characterize droplet size distribution through laser diffraction

• Sensory Evaluation:

  • Conduct Temporal Dominance of Sensations (TDS) with attributes: creamy, smooth, thick, oily, watery
  • Include creaminess intensity rating on 15-point scale
  • Control for oral physiology through saliva flow rate measurement
  • Use warm water (35°C) rinses between samples

• Data Analysis:

  • Construct Multiple Linear Regression model with creaminess as dependent variable
  • Include interaction terms between viscosity and boundary friction
  • Apply variable importance in projection (VIP) scoring to identify key predictors
  • Validate model with external sample set

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Instruments for Texture Correlation Studies

Category	Item	Function/Application	Key Manufacturers/Suppliers
Instrumentation	Texture Analyzer	Measures mechanical properties through compression, tension, and shear	Stable Micro Systems, AMETEK, Mecmesin [6]
	Rheometer	Characterizes flow and deformation behavior; measures viscosity	TA Instruments, Anton Paar, Malvern Panalytical
	Tribometer	Quantifies lubrication properties and friction coefficients	PCS Instruments, Anton Paar, TE77
Sensory Tools	Electronic Tongue (E-tongue)	Electrochemical sensor array for taste analysis; objective taste assessment	Alpha MOS, Insent, Sensa
	Electronic Nose (E-nose)	Volatile compound detection for aroma profiling	Alpha MOS, Airsense, Electronic Sensor Technology
	FaceReader	Facial expression analysis for emotional response to texture	Noldus Information Technology [58]
Reference Materials	Hydrocolloid Standards (e.g., xanthan, guar gum)	Viscosity calibration and thickness reference	Cargill, Ingredion, CP Kelco [7]
	Particle Size Standards	Graininess perception calibration and instrument validation	Malvern Panalytical, Microtrac, Horiba
	Sensory Reference Scales	Panel training and intensity calibration	Compusense, EyeQuestion, SensoMeter
Emerging Technologies	Virtual Reality Systems	Context-controlled sensory evaluation environments	Oculus, HTC Vive [58]
	Biomimetic Sensors	Artificial oral surfaces replicating human tactile response	Research-grade prototypes
	3D Food Printing Systems	Precise texture modification and customized structure creation	Natural Machines, Byflow

Advanced Applications and Case Studies

Beer Mouthfeel Optimization

The beer industry provides an exemplary case study in instrumental-sensory correlation, particularly for non- and low-alcoholic beers where mouthfeel deficiencies commonly limit consumer acceptance [1]. Successful correlation approaches include:

• Carbonation perception: Combining dissolved CO₂ measurement with acoustic emission analysis during bubble rupture
• Astringency prediction: Correlating polyphenol-protein interaction kinetics with temporal sensory dominance
• Fullness/body assessment: Relating ethanol content and residual extract parameters with sensory thickness and mouthcoating

Advanced breweries now implement inline rheometry coupled with multivariate process control to maintain consistent mouthfeel despite raw material variations.

Plant-Based Dairy Alternatives

The rapidly expanding plant-based sector relies heavily on texture replication through sophisticated correlation models:

• Creaminess prediction: Multimodal approach combining lubrication properties, particle size distribution, and viscosity parameters
• Mouthcoating persistence: Relating emulsion stability and interfacial composition to temporal sensory profiles
• Grittiness minimization: Correlating particle size thresholds (typically <15-20μm) with graininess detection probability

These models enable targeted ingredient selection and processing optimization without extensive consumer testing, significantly reducing development timelines.

Future Perspectives and Emerging Technologies

The field of instrumental-sensory correlation continues to evolve with several promising technological developments:

• Artificial Intelligence Integration: Machine learning algorithms, including artificial neural networks and random forest models, increasingly predict sensory preferences by identifying complex patterns in large datasets that traditional statistical methods miss [58]. These approaches excel at handling non-linear relationships and interaction effects between multiple instrumental parameters.

• Multi-Modal Sensing Platforms: Next-generation instruments combine simultaneous rheological, tribological, and acoustic measurements during simulated oral processing, capturing the temporal dynamics of structural breakdown [2]. These integrated systems provide comprehensive material characterization that better aligns with complex sensory perception.

• Biomimetic Sensor Development: Artificial oral surfaces with tunable mechanical properties and simulated salivary secretion more accurately replicate in-mouth conditions than conventional instrument geometries [2]. These systems incorporate biological components including mucin-coated surfaces and simulated tongue morphology.

• Digital Sensory Tools: Immersive technologies including virtual reality (VR) and augmented reality (AR) create controlled contextual environments that reduce situational bias in sensory evaluation [58]. Eye-tracking and facial expression analysis provide additional objective measures of consumer responses.

• Personalized Predictive Models: Accounting for individual physiological differences (saliva composition, oral physiology, genetic taste markers) improves correlation accuracy by segmenting populations based on biological determinants of perception.

Technology Evolution Path

Establishing meaningful correlations between instrumental and sensory data remains both a challenge and necessity for advancing food texture and mouthfeel research. Success requires integrated approaches combining appropriate instrumental techniques, well-designed sensory protocols, and sophisticated statistical modeling. As the field evolves, emerging technologies promise more accurate predictions through multi-modal measurement and artificial intelligence.

The continued refinement of these correlations will transform product development across food and pharmaceutical industries, enabling targeted texture design and reducing reliance on extensive human testing. By bridging the gap between physical measurement and human perception, researchers can ultimately create products that deliver superior sensory experiences while maintaining manufacturing efficiency and quality control.

Benchmarking and Competitive Product Analysis

In the competitive landscape of food and beverage products, texture and mouthfeel are decisive factors influencing consumer acceptance, repeat purchases, and overall market success [60]. While taste and aroma have traditionally been the focus of product development, research indicates that mouthfeel can be the differentiating factor when taste profiles are similar [60]. Astonishingly, more than 70% of consumers would consider switching brands if dissatisfied with a product's texture, highlighting its direct impact on commercial outcomes [61]. Mouthfeel encompasses the complex physical and chemical interactions experienced throughout the consumption process, from first bite to aftertaste [60]. For researchers and product developers, systematic benchmarking and competitive analysis of these attributes provide critical insights for achieving product superiority in increasingly crowded markets.

Understanding mouthfeel requires distinguishing it from related sensory concepts. Texture describes the mechanical and structural properties of food (e.g., firmness, crunchiness, viscosity) that can often be measured instrumentally, while mouthfeel represents the broader sensory perception resulting from these properties as experienced in the oral cavity [1]. This perception is multidimensional, integrating inputs from tactile, thermal, and chemical stimuli detected by the trigeminal nerve [1]. The integration of these signals fundamentally shapes the overall flavor experience and serves as an unconscious quality indicator for consumers [60] [62].

Foundational Concepts: Mouthfeel Versus Texture

Physiological Mechanisms of Mouthfeel Perception

Mouthfeel perception arises from a complex interplay between food components and physiological systems in the oral cavity. During consumption, foods release both volatile and non-volatile compounds that stimulate multiple sensory pathways [1]. Non-volatile compounds include tastants, irritants, and texture-related molecules that activate the gustatory and trigeminal systems [1]. The trigeminal nerve, in particular, serves as the primary conduit for tactile, thermal, and chemical sensations such as astringency, cooling, warming, and pungency [1].

Saliva plays a crucial role as a medium for dissolving flavor compounds and modulating textural sensations. Its composition and flow rate significantly influence how attributes like smoothness, astringency, and coating are perceived [1]. For instance, individuals with varying salivary profiles may experience the same product differently, presenting challenges for standardized sensory evaluation. The entire process of oral processing—including chewing, mixing with saliva, and temperature changes—dynamically alters food structure and consequently the evolving mouthfeel experience [1].

Key Mouthfeel Attributes and Vocabulary

A standardized vocabulary is essential for consistent mouthfeel evaluation across research and development teams. Key attributes can be categorized into mechanical, geometrical, and moisture-related characteristics:

• Hardness: The force required to achieve a given deformation or penetration [62].
• Fracturability: The force with which a material fractures (synonymous with brittleness) [62].
• Chewiness: The number of chews required to prepare a solid food for swallowing [62].
• Viscosity: The resistance to flow perceived in liquid or semi-solid products [60].
• Astringency: A complex sensation of dryness, puckering, and roughness throughout the oral cavity [1].
• Creaminess: A desirable combination of smooth, thick, and moist sensations associated with high-fat products [61].
• Carbonation: The tingling, prickling sensation from carbon dioxide release in beverages [1].

Methodologies for Competitive Benchmarking

Designing a Comprehensive Competitive Analysis Framework

Effective competitive product analysis begins with systematic identification and evaluation of benchmark products. The process should encompass both direct competitors (products in the same category with similar positioning) and indirect competitors (products that satisfy similar consumer needs but through different formulations or technologies) [63].

Initial Scoping Phase:

• Define Customer Problem: Identify the specific consumer need or sensory experience your product aims to address before selecting competitors [63].
• Identify Top Competitors: Select 3-5 primary competitors representing the current market landscape and consumer expectations [63].
• Gather Intelligence: Collect comprehensive data on competitors' product offerings, pricing, market positioning, and marketing claims through website analysis, product documentation, and third-party reviews [63].

Analysis Phase: For each competitor product, answer these critical questions [63]:

• What are their current product offerings and key features?
• How do they differentiate their products from competitors?
• What is their pricing strategy?
• What are the most liked features according to consumer feedback?
• What features do they lack that are in market demand?
• How do they market their products and communicate texture benefits?

Sensory Evaluation Protocols

Sensory analysis with trained panels remains the gold standard for mouthfeel assessment, providing the most direct correlation to consumer perception [60] [1].

Panel Selection and Training: Recruit panelists with demonstrated sensory acuity and subject them to extensive training using reference standards for key mouthfeel attributes. Training should continue until panelists demonstrate consistent scoring and high inter-rater reliability. For comprehensive profiling, panels typically require 8-12 trained participants [60].

Testing Protocol:

• Sample Preparation: Standardize sample preparation, serving temperature, and portion size across all evaluations. For solid foods, control for initial temperature and size uniformity.
• Serving Order: Utilize balanced serving orders (e.g., Williams Latin Square design) to mitigate carryover effects and position bias.
• Attribute Scaling: Employ structured scales (e.g., 15-point intensity scales) for quantifying specific mouthfeel attributes.
• Reference Standards: Provide physical reference standards for specific attributes (e.g., viscosity standards for thickness perception, tannin solutions for astringency).
• Data Collection: Use computerized data acquisition systems for real-time recording of sensory responses.
• Environmental Controls: Conduct evaluations in dedicated sensory booths with controlled lighting, temperature, and ventilation to minimize external influences [60].

Sensory Evaluation Comparative Table:

Evaluation Method	Key Applications	Panelist Requirements	Output Data	Limitations
Descriptive Analysis	Comprehensive mouthfeel profiling	Highly trained (50+ hours)	Quantitative attribute intensities	Time-intensive, expensive
Temporal Dominance of Sensations	Tracking changing mouthfeel over time	Trained	Sequence of dominant attributes	Complex data analysis
Consumer Acceptance Testing	Linking mouthfeel to preference	Untrained consumers	Hedonic ratings, preference maps	Does not provide diagnostic attribute data

Instrumental Analysis Methods

Instrumental methods provide objective, reproducible data that can complement sensory evaluation. Texture analyzers are particularly valuable for quantifying mechanical properties that correlate with specific mouthfeel attributes [62].

Texture Analysis: Texture analyzers function by applying controlled forces to food samples using various probes and fixtures, measuring parameters such as force, distance, and time [62]. These instruments can perform compression, extension, cutting, extrusion, bending, and shearing tests, generating force-time curves that reveal fundamental textural properties [62].

Table: Common Texture Analysis Tests and Their Mouthfeel Correlations:

Test Type	Probe/Fixture	Measured Parameters	Correlated Mouthfeel Attributes	Example Applications
Compression	Cylindrical probe	Hardness, springiness, cohesiveness	Firmness, tenderness	Bread, gels, cheese
Puncture	Needle/sphere probe	Peak force, gradient	Hardness, toughness	Fruits, vegetables
Shear	Warner-Bratzler blade	Maximum shear force	Tenderness, chewiness	Meat products
Extrusion	Back extrusion cell	Extrusion force, consistency	Thickness, spreadability	Semi-solids, sauces
Tensile	Tensile grips	Tensile strength, extensibility	Stringiness, stretchability	Cheese, dough

Advanced Instrumental Techniques:

• Rheology: Measures flow and deformation properties, particularly relevant for liquid and semi-solid products [1].
• Tribology: Assesses lubricating properties and thin-film rheology between surfaces, correlating with sensations like smoothness, creaminess, and astringency [1].
• Electronic Tongues: Array of non-specific sensors with partial selectivity to taste substances, useful for bitterness and umami assessment [1].
• Quartz-Crystal Microbalance (QCM) Sensors: Detect adhesion and deposition of food components on oral surfaces, relevant for astringency and coating perceptions [1].

Experimental Workflows and Data Integration

Integrated Mouthfeel Analysis Workflow

The following diagram illustrates a systematic approach to competitive benchmarking that integrates both sensory and instrumental methods:

Correlation Modeling Between Instrumental and Sensory Data

Establishing predictive relationships between instrumental measurements and sensory perception is crucial for efficient product development. The following workflow demonstrates this modeling process:

Statistical techniques such as Principal Component Analysis (PCA), Partial Least Squares Regression (PLSR), and Multiple Linear Regression (MLR) are employed to develop predictive models that can estimate sensory attributes from instrumental data alone. These models significantly reduce development time and costs by minimizing the need for extensive sensory testing during initial formulation stages.

The Researcher's Toolkit: Essential Materials and Reagents

Successful mouthfeel analysis and optimization requires specialized materials and ingredient systems. The following table details key research reagents and their applications:

Table: Essential Research Reagents for Mouthfeel Optimization:

Reagent Category	Specific Examples	Functional Role in Mouthfeel	Application Context
Hydrocolloids	Xanthan gum, gellan gum, pectin	Thickening, gelation, suspension, water-binding	Beverages, sauces, dairy alternatives
Starches	Functional native starches (e.g., Novation)	Viscosity building, texture modification, moisture retention	Plant-based meats, spoonable foods
Yeast Extracts	Umami-range yeast extracts	Kokumi activation, mouthfeel rounding, flavor enhancement	Reduced-salt products, savory applications
Fibers	Inulin, soluble corn fiber	Body building, sugar reduction, calorie reduction	High-fiber, low-sugar products
Protein Systems	Plant protein isolates, dairy proteins	Structure formation, gelation, foam stabilization	Protein-fortified foods, nutritional products
Mouthfeel Modifier Systems	Balchem's Injoy systems	Comprehensive mouthfeel enhancement via hydrocolloid/fat blends	Sugar-reduced beverages, functional drinks

These reagent systems are particularly valuable for addressing formulation challenges associated with clean-label trends, sugar reduction, and plant-based product development. For instance, yeast extracts can provide rounded mouthfeel and complexity while supporting clean label goals by replacing MSG [61]. Similarly, specialized starch systems enable the replication of textural properties typically provided by modified starches while maintaining a cleaner ingredient declaration [61].

Application Case Study: Non-Alcoholic and Low-Alcoholic Beer

The non-alcoholic and low-alcoholic beer category exemplifies the critical importance of mouthfeel in product acceptance. Alcohol contributes significantly to the viscosity, body, and mouth-coating properties of traditional beers, and its removal or reduction creates substantial mouthfeel challenges [1]. Consumers expect alcohol-free alternatives to maintain the sensory experience of their full-alcohol counterparts, particularly in attributes like carbonation sensation, smoothness, fullness, and afterfeel [1].

Competitive analysis in this category should focus on benchmarking key mouthfeel attributes:

• Carbonation perception: Prickling, tingling sensations and foam stability
• Body/Fullness: Weight and viscosity perception in the mouth
• Astringency: Dryness, puckering sensation from polyphenol-tannin interactions
• Smoothness: Absence of grittiness or harshness
• Afterfeel: Lingering sensations post-swallowing

Instrumental measurements such as viscosity, foam stability, and tribological properties can be correlated with sensory data to identify the mouthfeel gaps between alcoholic and non-alcoholic variants [1]. This approach enables targeted interventions using specific ingredient systems to build back the mouthfeel compromised by alcohol removal.

Strategic competitive benchmarking of mouthfeel and texture provides a powerful foundation for product differentiation and market success. By implementing the integrated methodologies outlined in this guide—combining rigorous sensory evaluation with objective instrumental analysis—researchers and product developers can identify critical texture gaps and opportunities in the competitive landscape. The resulting insights enable data-driven formulation strategies that align with evolving consumer expectations for both sensory quality and clean-label transparency. In an increasingly competitive market where texture often determines repeat purchase behavior, systematic mouthfeel benchmarking transitions from optional activity to strategic necessity.

Texture is a fundamental sensory attribute defined as "all the mechanical, geometrical, surface and body attributes of a product perceptible by means of kinaesthesis and somesthesis receptors" according to the International Organization for Standards (ISO) [64]. This complex multidimensional property is equally essential to our eating experience as flavor and smell, encompassing characteristics from the creaminess of custard to the fizz of foam and the slurpability of soup [12]. Despite its importance, texture remains notoriously difficult to predict during the food development process, creating a significant challenge for researchers and product developers.

The accurate prediction and design of food texture carries substantial implications across multiple domains. For the food industry, it enables the creation of more appealing products while managing cost and ingredient quality [12]. More critically, texture can be a nutritional necessity for vulnerable populations. For people with difficulty swallowing, such as the elderly or stroke patients, achieving precisely calibrated texture is vital—too thin, and it can cause aspiration; too thick, and it can be difficult to consume safely [12]. The emerging integration of artificial intelligence (AI) and machine learning (ML) technologies is now poised to transform this challenging field, enabling smarter food design through more accurate texture prediction and optimization.

Traditional Texture Assessment and Its Limitations

Conventional Methodologies

Traditional texture evaluation encompasses both subjective and instrumental approaches. Sensory evaluation relies on human perception through dynamic assessment methods such as Time-Intensity (TI), Temporal Dominance of Sensations (TDS), and Temporal Check-All-That-Apply (TCATA) [2]. These methods provide valuable insights into texture profiles but are inherently subjective and variable.

Instrumental methods are categorized into three primary types [2]:

• Fundamental methods: Based on materials science, including rheology (flow and deformation) and tribology (friction and lubrication)
• Empirical methods: Experience-based measurements using devices like Texture Profile Analyzers, Warner-Bratzler shear cells, and puncture testers
• Imitative methods: Replicate oral processes to emulate human perception during eating

Key Challenges in Texture Prediction

Food development traditionally occurs in iterative cycles where recipes are created, cooked, tasted, and incrementally adjusted—a laborious, costly, and time-consuming process [12]. This approach faces several specific challenges in texture prediction:

• Subjectivity: Human tasting panels consist of testers with unique palates and preferences [12]
• Multimodal complexity: Different formulations and ingredients can result in the same perceived texture even if their physical properties differ [12]
• Material complexity: Non-Newtonian liquids like ketchup or yogurt flow differently under stress, complicating predictions [12]
• Instrumental-sensory disconnect: While instruments provide quantitative physical measurements, correlating these parameters with sensory texture perception remains challenging [2]

AI and Machine Learning Approaches for Texture Prediction

Core Machine Learning Methodologies

AI and ML technologies are increasingly applied to overcome the limitations of traditional texture assessment. Different algorithmic approaches offer distinct advantages for various texture prediction tasks:

Table 1: Machine Learning Algorithms for Texture Prediction

Algorithm	Best Application	Key Advantages	Performance Examples
Random Forest (RF)	Modeling sensory perception from chemical data [65]	Low computational effort; handles non-linear relationships [65]	>80% accuracy for wine mouthfeel prediction [65]
XGBoost	Sensory perception modeling with complex chemical inputs [65]	Slightly better predictive performance than RF [65]	>80% accuracy for wine mouthfeel prediction [65]
Sensory-based Autoencoders	Predicting texture perception with limited data [12]	Works with very small sample sizes; careful validation [12]	Accurate predictions using few bouillon samples [12]
Artificial Neural Networks (ANN)	Complex non-linear texture relationships [66]	Can model highly complex, non-linear relationships	High accuracy for surface area prediction in activated carbon (R²>0.96) [66]

Case Study: Sensory-Based Autoencoder for Mouthfeel Prediction

Research from Purdue University's Department of Food Science demonstrates a novel application of AI for texture prediction. The team developed a sensory-based autoencoder—a type of neural network designed to learn how humans perceive texture—that can accurately predict mouthfeel based on physical properties measurable in the lab [12].

Experimental Protocol:

• Data Collection: Utilized existing data from bouillon samples originally published in a Nature Communications paper examining perceived thickness of liquids [12]
• Model Architecture: Implemented a specialized autoencoder neural network designed to handle sensory data [12]
• Training Approach: Employed cross-validation statistical methods to divide limited data into multiple subsets for rigorous testing without overfitting [12]
• Validation: Tested model's ability to generalize from limited data points through careful validation protocols [12]

This approach addresses a critical limitation in food science research: the scarcity of large, labeled datasets. Unlike most machine learning models that require massive data sets, this system can work with very small sample sizes while maintaining reliability [12]. The model successfully establishes the complex link between quantitative physical properties and subjective sensory feelings that has traditionally challenged food scientists [12].

Case Study: Predicting Wine Mouthfeel from Chemical Measurements

Another significant application comes from research modeling red wine mouthfeel from simple chemical measurements. This study comprehensively investigated bagging and boosting machine learning algorithms for predicting sensory properties from chemical data [65].

Experimental Protocol:

• Sensory Evaluation: A panel of 15 winemakers described the mouthfeel properties of 30 commercial red wines using rate-all-that-apply methodology [65]
• Chemical Analysis: Parallel acquisition of linear sweep voltammetry signals, excitation-emission matrix, and absorbance data [65]
• Data Processing: Principal component analysis simplified interpretation of sensory variables [66]
• Model Development: Compared Random Forest and XGBoost algorithms against classical PLS modeling [65]
• Validation: Rigorous testing of predictive performance on unseen data [65]

The research identified four independent sensory dimensions that differentiated wines: 'drying', 'full body', 'velvety', and 'gummy' [65]. The ML algorithms exhibited strong predictive performance on test data, with an average value exceeding 80% accuracy across all three sets of chemical variables employed [65].

AI and Machine Learning Workflow for Texture Prediction

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Research Tools for AI-Driven Texture Analysis

Tool/Category	Specific Examples	Function in Texture Research
Rheological Instruments	Rheometers, Texture Profile Analyzers [2]	Measure mechanical properties related to stress and strain (hardness, viscosity, elasticity)
Tribological Instruments	Tribology cells, mounted tribological devices, soft texture analyzer-tribometer (STAT) [2]	Characterize surface properties and lubrication behavior relevant to mouthfeel
Chemical Sensing Technologies	Linear sweep voltammetry, absorbance spectroscopy, excitation-emission matrix (EEM) [65]	Acquire chemical data correlated with sensory properties
Advanced Imaging	Hyperspectral imaging, multispectral imaging [64] [67]	Non-destructive analysis of food structure and composition
Computational Resources	Python/R with scikit-learn, TensorFlow, PyTorch; High-performance computing resources [12] [65]	Implement ML algorithms for texture prediction

Implementation Framework and Experimental Design

Data Requirements and Preparation

Successful implementation of AI for texture prediction requires careful attention to data quality and structure:

• Data Collection: Compile comprehensive datasets pairing instrumental measurements with sensory evaluations [65]
• Feature Engineering: Extract relevant features from raw data, including First-Order Statistical (FOS) and Gray-Level Co-occurrence Matrix (GLCM) features for imaging data [68]
• Data Normalization: Scale extracted features between 0 and 1 to ensure model stability [68]
• Cross-Validation: Employ techniques like k-fold cross-validation to ensure model reliability with limited data [12]

Integration with Existing Food Development Workflows

AI-driven texture prediction shows particular promise when integrated into established food development processes. Traditional approaches to creating new foods involve complex, iterative cycles that combine food science, engineering, culinary art, and consumer research [69]. For example, developing a new plant-based meat product requires defining the target product, selecting ingredients, developing formulations, engineering texture, and optimizing the final product [69]. AI can accelerate this process by efficiently screening multimodal parameter spaces to identify promising combinations for ingredient selection, formulation development, texture engineering, and product optimization [69].

Traditional vs. AI-Enhanced Food Development

Future Directions and Research Opportunities

The field of AI-driven texture prediction continues to evolve with several promising research directions:

• Multimodal Data Integration: Combining rheological, tribological, and tactile sensing data to deepen understanding of sensory texture perception [2]
• Advanced Algorithms: Exploring generative AI and large language models to create entirely new formulations based on natural language prompts [69] [70]
• Personalized Nutrition: Developing texture-modified foods tailored to individuals with specific dietary needs or swallowing difficulties [12]
• Sustainability Focus: Optimizing plant-based and alternative protein products to mimic the texture of animal-based foods [69]
• Explainable AI: Improving model interpretability to understand the physicochemical basis of texture perception [64] [71]

Despite significant progress, important challenges remain. Data correlating formulation to rheology, texture, and flavor are still rare, and labeled, structured datasets are often proprietary [69]. Future research should focus on creating more extensive, high-quality datasets and developing transfer learning approaches that can leverage knowledge across different food systems.

AI and machine learning technologies are fundamentally transforming the field of food texture prediction. By establishing robust correlations between measurable physical properties and subjective sensory perceptions, these approaches enable more efficient, precise, and targeted food design. From Random Forest and XGBoost algorithms that predict wine mouthfeel with over 80% accuracy to specialized autoencoders that work with limited data, the methodological advances demonstrate significant potential to accelerate food innovation.

As these technologies continue to mature, they promise to democratize food innovation, making it possible to design foods that are not only more appealing but also address specific nutritional needs and sustainability challenges. The integration of AI into texture prediction represents a paradigm shift from traditional trial-and-error approaches to data-driven, predictive food design—a transition that is critical for building healthier, more sustainable, and more personalized food systems for the future.

Within the broader context of food texture and mouthfeel research, the development of tailored foods for biomedical applications represents a frontier where sensory science directly impacts clinical outcomes. Mouthfeel, the complex multidimensional sensory experience encompassing tactile, thermal, and mechanical sensations in the oral cavity, plays a crucial role in food acceptance and consumption, particularly for patients with specific physiological limitations [1]. For populations with dysphagia, metabolic disorders, or age-related sensory decline, the traditional focus on nutritional content alone is insufficient; the structural and sensory properties of food must be precisely engineered to ensure both safety and compliance [12]. This technical guide explores the cutting-edge methodologies, material sciences, and computational approaches enabling the next generation of clinically tailored foods, with particular emphasis on the translation of mouthfeel research into therapeutic applications.

The integration of sensory science with biomedical engineering allows for the creation of foods that not only meet nutritional requirements but also address the physiological and psychological barriers to consumption in clinical populations. For elderly or stroke patients with swallowing difficulties, texture is not merely a matter of preference but a critical safety factor—too thin liquids can cause aspiration, while overly thick consistencies can be difficult to consume safely [12]. Similarly, for individuals following restricted diets for metabolic conditions, the successful mimicry of indulgent mouthfeels can significantly improve dietary adherence. The following sections provide a comprehensive technical framework for developing these specialized food products, from fundamental material properties to advanced manufacturing protocols.

Key Application Areas for Tailored Foods

Dysphagia Management and Texture-Modified Foods

The development of foods for dysphagia management requires precise control over rheological and textural properties to prevent aspiration while maintaining palatability. Hydrocolloid systems comprising starches, gums, and gelatins are employed to modify viscosity and create safe swallowing consistencies. Key technical challenges include maintaining structural integrity during oral processing while ensuring rapid disintegration in the esophagus. Sensorially, these systems must balance the absence of choking hazards with the perception of quality and freshness, often compromised in traditional texture-modified foods [1]. Clinical protocols require testing with instrumental rheometry alongside sensory panels to validate safety profiles, with particular attention to yield stress and shear-thinning behavior that mimics the bolus formation of natural foods.

Plant-Based Biomedical Formulations

The growing field of plant-based biomedical formulations addresses both ethical concerns and specific clinical needs, such as dairy or meat allergies requiring elimination diets. Mouthfeel mimicry presents significant technical challenges in replicating the creamy richness of dairy or the fibrous tenderness of meat using plant-derived proteins and stabilizers [29]. Current research focuses on the precision structuring of plant proteins through thermodynamic processes to achieve target textures. For example, surrogate seafood products are being developed to provide alternative sources of omega-3 fatty acids while replicating the flakiness and chewiness of fish flesh [12]. These formulations require sophisticated approaches to mask off-flavors associated with plant proteins while achieving the multi-layered mouthfeel consumers expect.

Age-Related Nutritional Support

Geriatric nutrition demands specialized foods addressing both physiological decline in sensory perception and age-related masticatory limitations. Product development in this area focuses on enhancing flavor release while maintaining soft textures that require minimal chewing effort. The trend toward "blissfully boosted" foods combines indulgence with functional benefits, incorporating protein enrichment, probiotics, or nutrient fortification without compromising sensory properties [29]. Technical approaches include controlled heterogeneities in food matrices to enhance flavor perception despite reduced salivary flow, and the application of tribology principles to optimize lubricity for easier swallowing.

Table 1: Biomedical Food Applications and Key Mouthfeel Parameters

Application Area	Critical Mouthfeel Parameters	Target Patient Populations	Formulation Challenges
Dysphagia Management	Viscosity, cohesiveness, hardness, adhesiveness	Stroke patients, elderly, neurodegenerative diseases	Safety-sensory trade-off, temperature stability, nutrient retention
Metabolic Disorders	Creaminess, richness, satiety perception	Diabetes, obesity, PKU	Low-energy dense textures, sugar/fat replication, glycemic control
Pediatric Formulations	Familiarity, fun textures, flavor-texture congruence	Children with feeding disorders, allergies	Age-appropriate sensory appeal, nutrient density, compliance
Geriatric Nutrition	Lubricity, softness, flavor enhancement	Elderly with chewing difficulties, sensory impairment	Palatability-nutrient density balance, ease of preparation

Emerging Research and Technological Enablers

Machine Learning in Texture Prediction

A groundbreaking advancement in the field comes from Purdue University's Transport Phenomena Laboratory, where researchers have developed an artificial intelligence model to accurately predict texture perception based on measurable physical properties of food [12]. This sensory-based autoencoder—a type of neural network—learns the complex relationship between quantitative material measurements and human sensory perception, effectively bridging the gap between laboratory instrumentation and subjective mouthfeel assessment. The model was successfully trained using limited data from bouillon samples, employing cross-validation statistical methods to ensure reliability from small sample sizes—a common constraint in clinical food development [12]. This approach allows researchers to reverse-engineer foods for specific clinical needs by predicting how texture will be perceived without continuous cycles of prototype development and human testing.

Advanced Material Science for Mouthfeel Design

The design of tailored foods increasingly relies on advanced material science principles to control structural properties at multiple length scales. Multi-layered mouthfeel represents a key innovation trend, combining contrasting textures (e.g., crunchy with soft, dry with wet) within a single product to create enhanced sensory experiences [29]. This approach is particularly valuable in clinical nutrition where sensory fatigue from monotonous textures can lead to reduced intake. Technical implementation involves sequential deposition techniques, encapsulation, and controlled hydration barriers to maintain discrete textural zones until oral processing begins. Additionally, the manipulation of air incorporation through foaming, puffing, or nitro-infusion creates physically lighter foods that provide indulgent and calming mouthfeels—an important consideration for patients with early satiety or volume restrictions [29].

Table 2: Research Reagent Solutions for Mouthfeel Engineering

Research Reagent	Functional Role	Technical Application	Mouthfeel Attribute
Hydrocolloids (starches, gums)	Thickening, gelling, water-binding	Dysphagia foods, sauce systems	Viscosity, sliminess, thickness
Plant Proteins (pea, soy, fava)	Protein enrichment, structure formation	Meat/dairy alternatives, nutritional supplements	Creaminess, chewiness, chalkiness
Dietary Fibers (inulin, beta-glucan)	Bulking, texture modification	Reduced-calorie foods, glycemic control	Fullness, fibrousness, graininess
Emulsifiers (lecithin, mono/diglycerides)	Interface stabilization, aeration	Creamy applications, foams	Smoothness, creaminess, richness
Gelling Agents (gelatin, pectin, carrageenan)	Matrix formation, moisture retention	Gels, desserts, texture-modified foods	Firmness, elasticity, meltaway
Crystalline Particles (sugar, salt, phytosterols)	Microstructure control, flavor release	Suspensions, coatings	Grittiness, crunchiness, crackliness

Experimental Protocols for Mouthfeel Analysis

Sensory Evaluation Protocol for Clinical Populations

Objective: To quantitatively assess the perceived mouthfeel characteristics of tailored foods using trained sensory panels with specific relevance to clinical applications.

Methodology:

• Panel Selection and Training: Recruit panelists (n=8-12) with demonstrated sensory acuity. For clinical relevance, include sub-populations with specific physiological conditions (e.g., age-related sensory decline, xerostomia) when ethically appropriate. Train panelists using reference standards spanning the target texture spectrum [1].
• Sample Preparation: Prepare test samples under controlled conditions (temperature, humidity). For solid foods, standardize cutting dimensions. For liquids, control for serving temperature and resting time. Utilize blind coding and randomized presentation order to eliminate bias.
• Testing Environment: Conduct evaluations in dedicated sensory booths with controlled lighting, temperature, and ventilation. Provide water and neutral crackers for palate cleansing between samples.
• Data Collection: Employ quantitative descriptive analysis (QDA) using structured scales for key mouthfeel attributes (e.g., thickness, smoothness, chewiness, afterfeel). Alternatively, use temporal dominance of sensations (TDS) to capture evolving texture perception during oral processing.
• Data Analysis: Apply analysis of variance (ANOVA) to identify significant differences in perceived attributes. Use multivariate techniques (PCA, CVA) to map the sensory space of products.

Technical Notes: For dysphagia applications, include safety parameters such as ease of swallow and residue perception. Adapt protocols for vulnerable populations following ethical guidelines [1].

Instrumental Texture Analysis Protocol

Objective: To establish correlative relationships between instrumental measurements and sensory perception of mouthfeel attributes.

Methodology:

• Texture Profile Analysis (TPA): Perform two-bite compression test using a texture analyzer equipped with a cylindrical probe. Calculate hardness, cohesiveness, springiness, gumminess, and chewiness from resulting force-time curve.
• Rheological Characterization: For fluid and semi-solid systems, employ rotational rheometry to determine viscosity profiles across shear rates (0.1-100 s⁻¹) relevant to oral processing. Conduct oscillatory measurements to characterize viscoelastic properties (G', G'').
• Tribological Measurements: Utilize soft tribology setups with polydimethylsiloxane (PDMS) surfaces to simulate oral contact and determine lubricating properties in the mixed and boundary lubrication regimes.
• Microstructural Analysis: Employ microscopy techniques (light, SEM, CLSM) to characterize structural elements contributing to mouthfeel at multiple length scales.

Technical Notes: Standardize sample preparation and loading procedures. Control for temperature effects, particularly for fat-based systems. Establish correlation models between instrumental and sensory data using multiple regression techniques [12].

In-Vitro Oral Processing Simulation Protocol

Objective: To simulate the structural breakdown of food during oral processing and quantify associated textural changes.

Methodology:

• Artificial Mouth System: Utilize a mechanical system incorporating temperature control (37°C), simulated salivary secretion (artificial saliva), and programmable compression-shearing actions mimicking mastication.
• Boluses Formation and Analysis: Subject test products to standardized oral processing sequences (varying by product type). Recover boluses at predetermined time points for analysis of particle size distribution, moisture content, and rheological properties.
• Time-Resolved Measurements: Employ in-line rheometry or texture analysis during the breakdown process to capture dynamic changes in mechanical properties.
• Flavor Release Monitoring: For integrated systems, incorporate membrane inlet mass spectrometry or proton-transfer-reaction mass spectrometry to quantify temporal release of volatile compounds during processing.

Technical Notes: Calibrate system parameters using in-vivo measurements of oral processing. Establish correlation between in-vitro breakdown patterns and sensory perception of attributes like pastiness, toothpacking, and clearance [1].

Future Research Directions and Challenges

The field of tailored foods for biomedical applications faces several significant challenges that represent opportunities for future research. Climate-proofing specialized foods presents a particular challenge, as rising temperatures during transport and storage can alter critical textural properties—chocolate may develop gritty particles, dairy products may sour, and frozen foods can experience ice crystal growth resulting in unpleasant textures [29]. These stability issues require new approaches to thermal tolerance in formulations intended for clinical use, especially in regions with limited refrigeration infrastructure.

Additionally, the tension between clean-label trends and functional requirements necessitates innovation in ingredient technology. Patients and caregivers increasingly demand recognizable ingredients, yet achieving specific biomedical textural properties often requires specialized hydrocolloids and stabilizers [29]. Research into novel processing techniques like freeze-drying and high-pressure processing may provide alternative paths to desired mouthfeels without complex ingredient statements. Finally, personalized nutrition approaches will require understanding how individual differences in oral physiology, salivary composition, and sensory perception interact with tailored food designs—an area where the machine learning approaches discussed show significant promise for matching specific patient needs with optimal product specifications [12].

Conclusion

A robust understanding of food texture and mouthfeel is paramount for advancing both food science and clinical practice. The integration of foundational knowledge, precise methodological application, and strategic troubleshooting enables the development of foods that meet specific sensory and nutritional needs. The emergence of AI and machine learning promises to revolutionize texture design, moving from iterative testing to predictive modeling. For biomedical and clinical research, these advancements are pivotal. They pave the way for creating next-generation foods that can effectively manage dysphagia, combat malnutrition in vulnerable populations like the elderly, and potentially enhance drug delivery systems, ultimately improving patient outcomes and quality of life through tailored nutritional solutions.

References

• 1. Mouthfeel of Food and Beverages: A Comprehensive Review ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12235588/]
• 2. Methods and instruments for the evaluation of food texture [https://www.sciencedirect.com/science/article/abs/pii/S0963996925004995]
• 3. Review The sensory perception of texture and mouthfeel [https://www.sciencedirect.com/science/article/pii/092422449610025X]
• 4. Rheology - Texture and mouthfeel [https://people.brandeis.edu/~sekuler/SensoryProcessesMaterial/rheology.html]
• 5. Effect of Hydrocolloids on Penetration Tests, Sensory ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11820735/]
• 6. Food Texture Analyzer 2025 Trends and Forecasts 2033 [https://www.datainsightsmarket.com/reports/food-texture-analyzer-1569212]
• 7. Food Texture Market Report 2025 (Global Edition) [https://www.cognitivemarketresearch.com/food-texture-market-report]
• 8. Trigeminal Nerve [https://www.physio-pedia.com/Trigeminal_Nerve]
• 9. The Trigeminal Sensory System and Orofacial Pain - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC11508441/]
• 10. Neuroanatomy, Neural Taste Pathway - StatPearls - NCBI - NIH [https://www.ncbi.nlm.nih.gov/books/NBK545236/]
• 11. Trigeminal Sensations to enhance and enrich flavor ... [https://www.sciencedirect.com/science/article/pii/S2667268522000584]
• 12. From lab to table: Purdue Food Science research predicts ... [https://ag.purdue.edu/news/2025/05/from-lab-to-table-purdue-food-science-research-predicts-texture-with-machine-learning.html]
• 13. SIS Application of Quantitative Analytics to Data Testing Sensory [https://www.sisinternational.com/sis-application-of-quantitative-data-analytics-to-sensory-testing/]
• 14. Data visualization in the neurosciences - PubMed Central - NIH [https://pmc.ncbi.nlm.nih.gov/articles/PMC4427844/]
• 15. A “mouthfeel wheel” terminology for communicating the ... [https://www.sciencedirect.com/science/article/abs/pii/S0950329318309352]
• 16. Food Texture - an overview [https://www.sciencedirect.com/topics/food-science/food-texture]
• 17. Mouthfeel [https://en.wikipedia.org/wiki/Mouthfeel]
• 18. Glossary: Terminology of food texture | Mecmesin [https://www.textureanalyzers.com/glossary]
• 19. Mouthfeel - an overview | ScienceDirect Topics [https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/mouthfeel]
• 20. Geometric Properties: Size and Shape [https://link.springer.com/chapter/10.1007/978-3-540-34194-9_3]
• 21. Methodological Considerations in Saliva‐Based Biomarker ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12560811/]
• 22. Investigation on taste characteristics and sensory ... [https://www.sciencedirect.com/science/article/pii/S2213453023001404]
• 23. Saliva's hidden powers | Headline Science [https://www.acs.org/pressroom/headline-science/saliva-chemistry.html]
• 24. In Vitro Robotic Chewing Studies of Food Texture Changes ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12381518/]
• 25. Pharmaceutica-2025 | April 07-08, 2025 | Vancouver [https://drug-delivery.pharmaceuticalconferences.com/events-list/advances-in-oral-drug-delivery-systems-overcoming-barriers-and-enhancing-bioavailability]
• 26. GLP1 & Oral Peptides | April 15-16, 2025 | San Diego, CA [https://www.drugdiscoverychemistry.com/25/oral-peptides]
• 27. Correlating instrumental textural properties to human ... [https://www.sciencedirect.com/science/article/pii/S2665927125002023]
• 28. Experimental Study of Two-Bite Test Parameters for Effective ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12383982/]
• 29. 9 mouthfeel trends to watch in 2025 [https://www.foodnavigator.com/Article/2024/12/17/top-mouthfeel-innovations-to-watch-in-2025/]
• 30. Understand your Texture Profile Analysis tests and analysis [https://www.stablemicrosystems.com/blog/2025/understand-your-texture-profile-analysis-tests-and-analysis/]
• 31. Oral processing, texture and mouthfeel: From rheology to ... [https://www.sciencedirect.com/science/article/pii/S1359029413000587]
• 32. A Review of Processing Techniques and Rheological ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11798767/]
• 33. Modernising Orodispersible Film Characterisation to Improve ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9029462/]
• 34. Electronic tongue: An analytical gustatory tool - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC3312724/]
• 35. Electronic tongue and electronic nose for food quality and ... [https://www.sciencedirect.com/science/article/abs/pii/S0963996922012728]
• 36. Electronic Tongues and Noses: A General Overview [https://www.mdpi.com/2079-6374/14/4/190]
• 37. World's first artificial tongue 'tastes and learns' like a real ... [https://www.livescience.com/technology/worlds-first-artificial-tongue-tastes-and-learns-like-a-real-human-organ]
• 38. Chinese scientists develop artificial tongue to measure ... [https://www.globaltimes.cn/page/202511/1349196.shtml]
• 39. Practice guide for sensory panel training Part 1 [https://www.dlg.org/en/mediacenter/dlg-expert-reports/food-sensory-technology/dlg-expert-report-07-2017-practice-guide-for-sensory-panel-training-part-1]
• 40. Guidelines for Food and Beverage Sensory Analysis [https://flavorsum.com/sensory-analysis-guidelines-food-and-beverage/]
• 41. Preferred Attribute Elicitation (PAE) in the Sensory Descriptive ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12079308/]
• 42. How to conduct a Sensory Analysis test: the golden rules [https://www.puratos.com/blog/how-to-conduct-a-sensory-analysis-test-the-golden-rules]
• 43. From Sweetness to Mouthfeel: A Review on Overcoming ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12639530/]
• 44. Texture and flavor switchable emulsion gels for replaced ... [https://www.sciencedirect.com/science/article/abs/pii/S0308814625035174]
• 45. Texture and flavor switchable emulsion gels for replaced ... [https://pubmed.ncbi.nlm.nih.gov/40929993/]
• 46. How Food Texture Analyzer Works — In One Simple Flow ... [https://www.linkedin.com/pulse/how-food-texture-analyzer-works-one-simple-flow-xc76e/]
• 47. Dysphagia Foods: Current Research, Technological ... [https://www.sciencedirect.com/science/article/abs/pii/S0924224425004662]
• 48. Texture cannot be added: It's the architecture of mouthfeel [https://stablemicrosystems.com/blog/2025/texture-cannot-be-added-its-the-architecture-of-mouthfeel/]
• 49. A systematic review of the factors affecting textural ... [https://www.sciencedirect.com/science/article/pii/S0195666325003551]
• 50. Development of Texture-Modified Meat and Thickened Soup ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12294358/]
• 51. Swallowing behavior response to semi-solid foods ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12593691/]
• 52. Scientists create a mathematical model that explains ... [https://www.eurekalert.org/news-releases/1098454]
• 53. Mathematical Models Can Help Us Understand [https://www.nist.gov/blogs/taking-measure/mathematical-models-can-help-us-understand-and-possibly-treat-complex-diseases]
• 54. Effects of Replacing Skipped Meals With Oral Nutritional ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12321495/]
• 55. Food Oral Processing—An Industry Perspective - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC7899988/]
• 56. Optimal sensory evaluation protocol to model ... [https://www.sciencedirect.com/science/article/abs/pii/S096399691400324X]
• 57. Nutritional Intake in Hospitalized Patients Receiving ... [https://www.mdpi.com/2674-0311/4/3/40]
• 58. Innovative Approaches in Sensory Food Science [https://www.mdpi.com/2076-3417/15/8/4538]
• 59. An Overview of Sensory Characterization Techniques [https://pmc.ncbi.nlm.nih.gov/articles/PMC8834440/]
• 60. Why Mouthfeel Matters in Sensory Profiling [https://wssintl.com/mouthfeel-matters-sensory-profiling/]
• 61. “Taste doesn't lie”: Ingredient systems redefine mouthfeel ... [https://www.foodingredientsfirst.com/news/clean-label-texture-mouthfeel.html]
• 62. Texture Analysis - A Beginner's Guide [https://www.stablemicrosystems.com/BeginnersGuideToTextureAnalysis.html]
• 63. Competitive Product Analysis: The Full Guide [https://www.revuze.it/blog/competitive-product-analysis/]
• 64. A systematic review of data and models for predicting food ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12274706/]
• 65. Bagging and boosting machine learning algorithms for ... [https://www.sciencedirect.com/science/article/pii/S0950329325000692]
• 66. Leveraging machine learning for prediction and ... [https://www.nature.com/articles/s41598-025-95061-3]
• 67. Applications of Artificial Intelligence in Food Industry [https://www.mdpi.com/2304-8158/14/7/1241]
• 68. Machine learning and texture features based approach for ... [https://www.sciencedirect.com/science/article/pii/S1877050924009347]
• 69. AI for food: accelerating and democratizing discovery and ... [https://www.nature.com/articles/s41538-025-00441-8]
• 70. Future Foods | Food Informatics, Artificial Intelligence, and ... [https://www.sciencedirect.com/journal/future-foods/special-issue/106QLLW25M4]
• 71. Predicting multiple taste sensations with a multiobjective ... [https://www.nature.com/articles/s41538-024-00287-6]