Effect of micronization on selected volatiles of chickpea and lentil flours and sensory evaluation of low fat beef burgers extended with these micronized pulse flours
Bibliographic record
Abstract
The effect of micronization (at 130 and 150 °C) as a potential heat treatment to reduce ‘beany’ aroma and flavor of cooked chickpea (Cicer arietinum) and green lentil (Lens culinaris) flours was investigated. A simultaneous distillation solvent extraction method was developed to extract key volatile compounds with potential contribution to ‘beany’ aroma and flavor notes in micronized pulse flours and analyzed by gas chromatography-mass spectrometry. Concentrations of volatile compounds such as pentanol, hexanal, 2-hexenal, hexanol, heptanal, furan-2-pentyl, 2-octenal, nonanal, 2,4 decadienal, and 2,4- undecadienal were significantly (P<0.05) decreased with micronization. Low fat burgers fortified with 6% micronized chickpea and green lentil flours showed significantly higher acceptability for aroma, flavor, texture, color and overall acceptability (p<0.05) compared to non-micronized samples in a consumer acceptability test with 101 consumers. In addition, fatty acid analysis of burgers showed burgers containing micronized pulses had higher level of linoleic and linolenic acid content.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".