Application of a multivariate approach to the study of chemometric and sensory profiles of cookies fortified with brewers’ spent grain
Bibliographic record
Abstract
This work was aimed to investigate the effects of three factors on cookie quality: brewers' spent grain (BSG) composition [65% malted barley and 35% of unmalted durum (DA) or soft (RI), or emmer (EM) wheats]; geographical origin of the cereals used in brewing (Daunia or Salento); and percentages of BSG in cookie formulation (30 or 40%). A control made of 100% Manitoba flour was produced. Statistical analyses were performed to evaluate the effects of those factors (Analysis of Variance), the possibility to distinguish the various types of cookies (Principal Component Analysis), and the relationships among variables (Pearson Correlation Analysis).The single and interactive effects of the three factors were significant for almost all variables. Cookies with 40% EM spent grains showed the highest ash, dietary fibre, and total phenolic contents but cookies with 30% DA or RI spent grains received the highest overall quality scores due to the higher intensity of their fresh baked flavour and their lower hardness and fibrousness. Based on the nutritional and sensory characteristics, cookies fortified with RI and DA were the best to consume. Although few physicochemical differences can be attributed to geographical origin, a slightly higher overall sensory score was assigned to those produced with Salento cereals. Principal Component Analysis showed a clear separation between the control made of 100% Manitoba flour and the group of fortified cookies. Among the latter, the cookies produced with RI and DA spent grains were indistinguishable from each other due to their similar quality characteristics. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-024-06064-3.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".