Effects of Malting and Fermentation on Anti-Nutrient Reduction and Protein Digestibility of Red Sorghum, White Sorghum and Pearl Millet
Bibliographic record
Abstract
Sorghum and millet and their products require specialized treatment in order to improve their nutritive value, organoleptic properties and shelf-life. They contain anti-nutrients which are the major phytochemicals which negatively affects their nutritive values. The phytochemicals of concern include tannins and phytates, which interfere with mineral absorption, palatability and protein digestibility. Malting and fermentation treatments were applied to reduce the anti-nutrients, improve protein digestibility, and acidity to increase the products shelf life. The effects of malting and fermentation on the cereals nutritive value and anti-nutrient reduction were studied and evaluated for a period of 8 days. A treatment combining malting for 3 days and fermentation for 2 days respectively both at room temperatures (25°C) was employed. Tannins and phytates were significantly reduced (p ? 0.05) by malting and fermentation. Protein digestibility was significantly (p ? 0.05) improved by malting and fermentation treatments; malted cereals digestibility ranged between 34.5-68.1% while the fermented flours protein digestibility range was 97.4-98.3%. The pH values were lowered to below 4.0, a level at which they could effectively inhibit spoilage microorganisms at the end of the fermentation period. A combination of optimum time treatments of malting and fermentation for 3 days and 2 days respectively were effective in reducing tannins and phytates and improving protein digestibility of the cereals.
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 | 0.001 |
| 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".