USE OF A PANEL KNOWLEDGEABLE IN MATERIAL SCIENCE TO STUDY SENSORY PERCEPTION OF TEXTURE
Bibliographic record
Abstract
ABSTRACT This research assessed the relations between sensory and instrumental measures of the texture of solid foods when using a panel with previous knowledge of material science and fracture mechanics. Twelve commercial products varying in texture were evaluated by two panels; one panel was comprised of 11 engineering students who were familiar with material science, and a descriptive analysis panel of 15 experienced trained panelists. The engineering panel evaluated the products for attributes of hardness, stiffness, brittleness, viscoelasticity and toughness, while the descriptive panel evaluated the samples using terms generated through free choice profiling. Analysis of the data showed that texture evaluations of the products were consistent between the two panels. Certain mechanical properties such as hardness and stiffness were closely related to instrumental measures. However, other measures such as toughness were not well correlated with instrumental measures. PRACTICAL APPLICATIONS The applications of this research are twofold. First, this research shows that panelists, regardless of experience, use similar words with similar meanings during the assessment of texture of solid foods. Second, this research also more closely aligns sensory measures with material science by showing that sensory scores may be related to instrumental measures of texture in various ways. Sensory properties, such as hardness and stiffness, can be directly measured using a material science approach. Indirect relations between sensory panel scores and instrumental measurements may also exist, e.g., between crisp and crunchy. Lastly, instrumental measures may not adequately measure sensory perception of textures, particularly those related to toughness and viscoelasticity.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".