Volatile Flavor Profile of Saskatchewan Grown Pulses as Affected by Different Thermal Processing Treatments
Bibliographic record
Abstract
The objective of this study was to identify and quantify the volatile flavor composition of selected Saskatchewan grown pulses including navy beans, red kidney beans, green lentils, and yellow peas, and to determine the flavor changes induced by thermal processing. Flavor profile of roasted flours, ground roasted seeds, pre-cooked seeds, pre-cooked slurries, pre-cooked–freeze-dried, and pre-cooked–spray-dried flours was studied using headspace solid-phase microextraction gas chromatography/mass spectrometry. The highest total area count (p < 0.05) was found in navy bean and the lowest in red kidney bean. 3-Hexanol was the most abundant volatile flavor compound. Pre-cooking significantly reduced (p < 0.05) volatile compounds total area count by 61.75%, except for the red kidney bean and yellow pea, whereas roasting significantly increased (p < 0.05) total area count for navy bean and red kidney bean. Major differences observed in relative peak area for the same chemical family showed that volatile flavor compounds of pulses were significantly affected by type and processing conditions. Basic knowledge of the volatile profiles of pulses and the flavor changes occurred following different types of thermal processing, could ensure better quality control of raw materials and help product developers meet flavor-delivery challenges. The relevant information may also be of interest to relevant industries targeting specific pulse-based food product development.
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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".