Effect of combined germination and spontaneous fermentation on the bioactive, mineral, and microbial profile of red sorghum and pearl millet flours
Bibliographic record
Abstract
The aim of the study was to evaluate the influence of combined germination and spontaneous fermentation on the phytochemical, mineral, and microbial profile of red sorghum (Sorghum bicolor (L.) Moench) and pearl millet (Pennisetum glaucum (L.) R.Br.) flours. The analysis of the studied flours were performed by molecular spectrophotometry and flame atomic absorption spectrophotometry, respectively. Bioavailability of the iron, zinc, and calcium was estimated using the phytates/minerals molar ratios. The microbial load was counted using culture media specific to total bacteria, lactic acid bacteria, yeast and moulds, and Gram-negative bacteria. Combined processing resulted in flours exhibiting the lowest (p ˂ 0.05) total phenols, flavonoids, 3-deoxy-anthocyanidins, phytates contents, and DPPH scavenging activity and the highest (p ˂ 0.05) ABTS inhibition, mineral contents, and bioavailable iron, zinc, and calcium contents. Combined processing also resulted in the highest (p ˂ 0.05) microbial cell counts, with lactic acid bacteria being the most abundant species in both processed cereals. Combining germination and spontaneous fermentation of red sorghum and pearl millet flours could be a useful and simple processing technique to develop food products that alleviate mineral deficiencies and promote human health.
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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".