Elicitation kinetics of phenolics in common bean (<i>Phaseolus vulgaris</i>) sprouts by thermal treatments
Bibliographic record
Abstract
Abstract Phenolics are plant secondary metabolites with numerous health benefits, produced via the phenylpropanoid pathway in plants in response to environmental conditions. In this study, the mathematical relationship between thermal elicitation (25°C, 30°C, 35°C, and 40°C) of phenolic compounds through the accumulation of oxidative stress markers (hydrogen peroxide [H2O2], malondialdehyde [MDA], catalase [CAT], and guaiacol peroxidase [GPX]) and activation of phenylpropanoid triggering enzymes (phenylalanine ammonia‐lyase [PAL] and tyrosine ammonia‐lyase [TAL]) was kinetically modeled at different sprouting stages of common bean. The rate of H2O2 and MDA formation showed an increasing trend with an increase in sprouting temperature. However, activation rates of CAT, GPX, PAL, and TAL were highest at 30°C, after which there were significant reductions. Also, activation rate of PAL was lower as compared with TAL, which was further established with its low activation energy Ea value of 150 kJ/mol compared with TAL (221 kJ/mol). Also, activation energy values for total phenolic acids (30.4 kJ/mol) and flavonoids (64.0 kJ/mol) showed that they required less energy for formation during sprouting, compared with anthocyanins (209 kJ/mol), with the activation energy results obtained from their estimated kinetic rate constants and production percentages. Thus, manipulation of sprouting temperature can increase the potential use of common beans as natural functional foods with improved health benefits.
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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) | 0.000 | 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 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".