Sensory Assessment of Pressure‐Cooked and Pureed Pulses Reveals Similarities for Chickpea/Yellow Pea and Dry Bean/Faba Bean
Bibliographic record
Abstract
ABSTRACT The dominance of pulses in plant‐based protein foods necessitates an investigation into their organoleptic properties. Taste, aroma, flavor, and trigeminal attribute intensities of purees obtained from pressure‐cooked black bean, chickpea, faba bean, green lentil, pinto bean, and yellow pea were assessed using a trained panel. The panelists rated the attribute intensities using an interval scale with not perceptible and very intense as the anchors. The pulse‐like attribute was consistently rated highly across all the pulse purees. The black, pinto, and faba bean purees mostly exhibited similar characteristics, while chickpea and yellow pea purees behaved similarly, as revealed by analysis of variance and principal component analysis. For example, the dry bean and faba bean purees had higher intensities for the bitter, earthy, and metallic aromas and lower intensities for the green, floury/starchy, sweet, and nutty aromas than those for the chickpea and yellow pea purees. Interestingly, green lentil puree largely exhibited intensities that were typical of dry bean purees, especially pinto. In general, black bean, chickpea, faba bean, green lentil, pinto bean, and yellow pea purees were notable for metallic, sweet, sour, fruity, nutty, and green notes, respectively. The relative similarities in sensory profiles of the pulses can be useful to first, the food industry to expand on ingredients in formulations without drastic effects on the sensory quality and to market pulses that consumers may not be familiar with. Second, consumers who are neophobic might feel encouraged to try different pulses having comparable sensory characteristics with those they are familiar with.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".