A multifaceted approach to understanding the cooking behaviour of Canadian wonder common beans (Phaseolus vulgaris)
Bibliographic record
Abstract
In a quest to better understand the cooking behaviour of Canadian wonder common beans (Phaseolus vulgaris), matrix segmentation into the compositionally distinct components, cotyledons and seed coats was explored. This approach was aimed at providing insight into localised changes during thermal processing unlike averaged effects obtained when the whole bean matrix is considered. Development of hard-to-cook (HTC) in legumes in general and common beans in particular is widely known and the need for mechanistic insights into its influence on the cooking behaviour of common beans cannot be overemphasised. Therefore, in the current study, a methodological approach involving comparison of HTC and easy to cook (ETC) beans of either similar or different texture was utilized. Thermal processing of plant-derived foods triggers and/or enhances modifications of polymers that govern their structure and texture. For starchy, plant-derived matrices such as beans, these modifications involve mainly starch (gelatinisation) and pectin (solubility) as well as changes in (distribution of) other biomolecules. Therefore, a multifaceted approach integrating structural, textural, polymeric and micronutrient distribution changes was applied using microscopic, texture analysis, colorimetric, chromatographic and spectrometric techniques. Results from texture analysis showed that compared with seed coats, cotyledons played a larger role in HTC development and cooking behaviour of beans. Structural and polymeric analysis revealed a larger influence of pectin solubility than starch gelatinisation on cooking behaviour, with the former being more affected by HTC compared to the latter. Although changes in distributions of mineral ions were observed, most minerals were largely retained in either the seed coats or cotyledons. Findings from the multifaceted approach explored in this study facilitate construction of a cooking map for common beans.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".