Composition of Phenolic Acids and Antioxidant Properties of Selected Pulses Cooked with Different Heating Conditions
Bibliographic record
Abstract
Pulses are recommended for healthy eating due to their high content of nutrients and bioactive compounds that can undergo changes during cooking. This study investigated the effects of four cooking methods (boiling, pressure, microwave, slow) and three heating solutions (water, salt, sugar) on the phenolic acids and antioxidant properties of three pulses (faba beans, lentils, peas). The composition of phenolic acids differed among the three pulses with p-coumaric and ferulic being the dominant acids. Cooking increased free phenolic acids and lessened bound phenolic acids in faba beans and peas, while decreased both free and bound phenolic acids in lentils. Cooking resulted in reductions in total phenol content (TPC) in faba bean methanol and bound extracts. Pressure and microwave cooking increased TPC in lentil methanol extracts, while pot boiling and slow cooking reduced TPC. Microwave cooking resulted in increases in TPC in bound phenolic extracts from lentils. For peas, cooking increased TPC in both methanol and bound phenolic extracts. Significant changes were also observed in the antioxidant capacity of cooked pulses based on the scavenging ability of DPPH, ABTS and peroxyl radicals subject to the type of pulse, polyphenol and antioxidant assay. Despite the significant reduction in antioxidants, high amounts of phenolics with potent antioxidant activities are still found in cooked pulses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".