Impact of household food processing strategies on antinutrient (phytate, tannin and polyphenol) contents of chickpeas (<i><scp>C</scp>icer arietinum </i><scp>L</scp>.) and beans (<i><scp>P</scp>haseolus vulgaris </i><scp>L</scp>.): a review
Bibliographic record
Abstract
Summary Pulses, which include beans and chickpeas, are major constituents of the human diet. They are important sources of energy and nutrients, particularly protein, folate and minerals. However, they also contain antinutrients which bind minerals, mainly iron and zinc, rendering them less bioavailable or unavailable for absorption. The levels of these antinutrients may be reduced by food processing techniques such as soaking and germination. Researchers have used these techniques in a number of studies; however, there is no consensus regarding the optimum processing conditions for reduction in the levels of these antinutrients. Thus, this review was conducted to describe the results of studies on soaking and germination of chickpeas and beans. A systematic search was carried out utilising Food Science and Technology Abstracts ( FSTA ) (1969 to present), Web of Science (1899 to present) and Scopus (1823 to present). A total of thirty‐three articles were reviewed. Both soaking and germination resulted in significant but variable degrees of reduction in levels of antinutrients in most studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".