Characterization of the Fermented Milk “Laban” with Sensory Analysis and Instrumental Measurements
Bibliographic record
Abstract
ABSTRACT Sensory, chemical, and rheological properties of commercial and traditional laban samples were investigated to characterize this fermented milk. One commercial sample and 14 traditional samples, collected from various geographical locations in Lebanon, were evaluated by a descriptive panel in terms of appearance, color, texture, odor, taste, and after‐taste. Principal component analysis of the sensory data revealed high differences between laban samples. They were separated into 5 distinct groups that were identified by the following sensory characteristics: firm and sour, slimy and sweet, high butter odor, high yogurt odor, and moderate levels for all the descriptors. Six samples, showing different features, were selected from these 15 samples. Physicochemical analyses of acidity, lactose and fat contents, firmness, and apparent viscosity were assessed on these 6 samples. Laban, independently of its origin, displayed higher acidity than yogurt. Commercial laban showed higher acidity and viscosity than traditional samples. Statistical relationships between sensory and instrumental data showed significant correlation between apparent viscosity and smoothness, fat content and butter odor, titratable acidity and sourness, and penetrometry readings and sliminess. Finally, principal component analysis of the instrumental and sensory parameters revealed that both analyses characterized the samples in the same way.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".