Effect of Postharvest Practices (Sorting & De-hulling) on Total Mineral (Ash), Zinc and Iron Contents of Chickpea and Faba Bean Flours
Bibliographic record
Abstract
Objectives: Various factors influence utilization and nutrient content of pulses including preharvest and postharvest practices. Pulses are usually exposed to postharvest practices (harvesting, cleaning, sorting, drying, de-hulling, processing). The effect of these practices on the micronutrient content is still less studied. Understanding the influence of postharvest practices on micronutrient content will help to consider further food processing and other intervention methods. Methods: In this study chickpea (local and improved variety) and faba bean (local variety) were considered. The samples were exposed to sorting and de-hulling practices in laboratory. Sorting was done manually, while de-hulling was done with impact de-huller machine. Treated samples were milled in hammer miller and flour samples were analyzed for contents of ash (total mineral), zinc (Zn) and Iron (Fe). Results: The result showed that Zn and Fe contents of local chickpea, improved chickpea and faba bean were significantly different (p<0.05). Faba bean was higher in Zn and Chickpea was higher in Fe. There was no statistical difference in ash contents. The postharvest practices influenced the Fe content of improved chickpea and ash content of local chickpea. Sorting followed by de-hulling and de-hulling have reduced Fe content of improved chickpea and ash content of local chickpea. Conclusions: During formulation, processing and preparation of pulse based foods, less intensive
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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) | 0.001 | 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".