Antioxidant and Antimutagenic Activities of Optimized Extruded Desi Chickpea (Cicer arietinum L) Flours
Bibliographic record
Abstract
The objective of this study was to evaluate the effect of optimized extrusion cooking process on antioxidant and antimutagenic properties of desi chickpea cultivars. Three desi chickpea cultivars (Brown-ICC3512, Red-ICC13124, Black-ICC3761) were studied. Extrusion was carried out in a single-screw extruder; the operation conditions were previously optimized to obtain maximum antioxidant activity in extruded chickpea flour: Extrusion temperature (ET) = 155°C, and screw speed (SS) = 240 rpm. The antioxidant activity (AOA) was evaluated using the oxygen radical absorbance capacity (ORAC) assay. Antimutagenic activity (AMA) of ground samples extracts was tested against 1-Nitropyrene (1-NP) with the Kado microsuspension assay using Salmonella typhimurium strain TA98. The total phenolic (TPC) and flavonoid (TFC) contents, AOA, and AMA of raw desi chickpea cultivars ranged from 1.31 to 1.35 mg GAE g-1 sample, dw, from 0.464 to 1.006 mg CAE g-1 sample, dw, from 54.9 to 57.3 mmol TE g-1 sample, dw, and from 57.8-62.3% inhibition, respectively. Brown-ICC3512 showed the highest TFC and AOA, while Red-ICC13124 had the highest AMA. The extrusion cooking process increased the TPC, AOA and AMA of whole desi chickpea grains in 5.3-9.2%, 9.9-12.2%, and 17.5-21.9%, respectively. The optimized extrusion cooking process is a recommended technology for increasing AOA and AMA in desi chickpea grains, which could be used as functional foods.
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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".