The Role of Proteins in the Sensory Perception/Organoleptic Properties of Food
Bibliographic record
Abstract
ABSTRACT Proteins are critical macromolecules within food systems which modulates a broad range of sensory attributes including flavor, texture, aroma, and visual appeal. This review presents a comprehensive exploration of the role's proteins play in defining organoleptic properties, with a focus on their structural hierarchies, functional interactions, and biochemical transformations. Flavor generation from proteins is governed by complex pathways such as the Maillard reaction, Strecker degradation, enzymatic hydrolysis, and lipid–protein interactions, leading to the formation of volatile and nonvolatile compounds central to food palatability. The structure of proteins from primary to quaternary structures, determines their interactions with flavor volatiles, and matrix constituents, thereby influencing attributes such as astringency, lubrication, and aroma release dynamics. Processing methods including thermal treatment, high‐pressure processing, and fermentation, induce conformational shifts in protein structure, resulting in altered textural properties and sensory perception. The emergence of alternative proteins sourced from plants, insects, etc. introduces novel compositional and sensory challenges due to their distinct amino acid profiles, solubility, and inherent flavor precursors. Additionally, the role of bioactive peptides and intrinsically sweet amino acids as taste modulators and flavor enhancers, and debittering techniques including enzymatic, chemical, and physical approaches are discussed. The influence of storage conditions on protein stability, aggregation behavior, and flavor retention is also critically assessed. Overall, this review highlights the key role of proteins in connecting food chemistry and sensory science, and emphasizes the need for combining food structure, and innovative food processing methods to create the next generation of tasty, protein‐rich foods that meet consumer preferences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".