High Temperature Rotational Rheology of the Seed Flour to Predict the Texture of Canned Red Kidney Beans (Phaseolus vulgaris)
Bibliographic record
Abstract
The pasting profile of starchy tissues is conventionally measured by recording the apparent viscosity (η) in heating/cooling cycles. However, conventional rheometers show critical limitations when the starch is embedded in compact protein-rich cotyledon matrices, as occurs in pulses. In this work, the pasting profile of 13 red kidney beans (Phaseolus vulgaris) from the same cultivar but different growing locations was investigated using a heating/cooling cycle at higher temperature (130 °C) and pressurized conditions, using both water and brine as cooking solvents. It was hypothesized that the continuous measure of η at these conditions of flours from the dry seed would correlate with the texture, as determined by the mini-Kramer cell, of the beans after the entire process of soaking and canning. Furthermore, mechanistic answers were obtained by investigating their composition (starch, protein, and ash content) and physical properties (water holding capacity, seed ratio and weight). Interestingly, as opposed to the pasting profile at 95 °C, pasting indicators at 130 °C, including trough and final viscosity, strongly correlated with starch and protein content, seed coat ratio and, remarkably, with the firmness of the beans after canning when brine was incorporated. These results clearly show that small beans with a high protein content would bring about a more compact matrix that restricts starch from swelling and results in canned beans with a hard texture, which can be predicted by a lower pasting profile of the whole bean flour.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".