Sensory and Instrumental Consistency of Processed Cheeses
Bibliographic record
Abstract
<p>The objective of this study was to evaluate the instrumental and sensory texture of seven cheeses, as well as correlate sensory measurements of texture with mechanical properties. The cheeses were composed of different types of basic mass (casein and whey proteins) and emulsifying salts. Instrumental analysis of texture was performed using the universal mechanical testing machine (Instron) for determining the properties of firmness, elasticity, adhesiveness, gumminess, chewiness and cohesiveness. Data was analyzed using the principal component analysis and clustering analysis. Sensory texture was evaluated by a group of semi-trained assessors according to the ranking-difference test for texture of the products. The results were analyzed by the Friedman test; while sensory and instrumental texture measurements were correlated by the Spearman correlation coefficient. With regard to sensory and instrumental texture of the cheeses, the formation of three groups was observed: a first group consisting of cheeses with intermediate texture, another consisting of softer products and a third group formed of more consistent cheeses. Texture differences of the cheeses were determined by their protein and emulsifying agent composition. Sensory consistency presented a significant correlation (|r<sub>s</sub>| &gt; 0.90 and p &lt; 0.01) with the properties of mechanical: firmness, adhesiveness, chewiness and elasticity. On the other hand, the sensory texture measurement is not correlated with the instrumental measurements of gumminess and cohesiveness, indicating that they do not reflect the human perception of cheese texture.</p>
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".