Effect of Postharvest Practices (Sorting & De-hulling) on Total Mineral (Ash), Zinc and Iron Contents of Chickpea and Faba Bean Flours
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it