Sensorial, textural, and rheological analysis of novel pistachio‐based chocolate formulations by quantitative descriptive analysis
Bibliographic record
Abstract
Principal component analysis (PCA) was used to investigate the effects of pistachio oil (7.5 and 15%), xanthan gum (0 and 0.3%), distillated monoglyceride (0.5 and 1%), and cocoa butter (7.5 and 15%) on the sensorial descriptors of spread based on pistachio oil. The response variables were the most significant spread texture attributes: hardness, graininess, meltability, adhesiveness to spoon, adhesiveness to mouth, spreadability, fluidity, and oiliness. PCA revealed that the first two principal components explained 90% or more of the variance between the data. The first principal component was dominated by the descriptors' adhesiveness and hardness on the positive side and the descriptors' oiliness and fluidness on the negative side. The descriptor spreadability had a high positive loading on the second principal component. Herschel-Balkley and power law models were fitted to confirm the sensory evaluation results on different formulations. In the current research, the power law model seemed to be more accurate for fitting the samples. In terms of the selected texture attributes determined by the sensory evaluation, using component plot, the optimum combination of variables was found as follows: 15 pistachio oil, 7.5% cocoa butter, 0.3% xanthan gum, and 1% distilled monoglyceride that produced desirable spreads that mimic commercial spread.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".