Effect of selective fermentation on nutritional parameters and techno-functional characteristics of fermented millet-based probiotic dairy product
Bibliographic record
Abstract
The primary goal of this study was to assess the effect of selective fermentation on the nutritional and techno-functional characteristics of fermented millet-skim milk-based product. The product was made with HHB-311 biofortified pearl millet (PM) flour, skim milk powder, and isolated cultures (either alone or in combination) of Limosilactobacillus fermentum MS005 (LF) and Lactobacillus rhamnosus GG 347 (LGG). To optimize fermentation time, time intervals 8, 16, and 24 h were explored, while the temperature was kept 37 °C. Results of protein digestibility showed that LF (16 h) and LGG (24 h) fermented samples had significantly higher (p < 0.05) protein digestibility of 90.75 ± 1.6% and 93.76 ± 3.4%, respectively, than that of control (62.60 ± 2.6%). Further, 16 h fermentation with LF showed enhanced iron (39%) and zinc (14%) bioavailability. The results suggested that LF with 16 h fermentation is most suitable for making millet-based fermented products with superior techno-functional attributes and micronutrient bioavailability.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".