Thermal treatments to partially pre‐cook and improve the shelf‐life of whole pearl millet flour
Bibliographic record
Abstract
Abstract BACKGROUND: Pearl millet flour is highly susceptible to rancidity during storage. Urbanization has created a demand for pearl millet flour with longer shelf‐life and short cooking time. To try to prevent rancidity and pre‐cook the flour, pearl millet grain was subjected to the thermal treatments of toasting, boiling, and toasting then boiling. RESULTS: Fat acidity of flour from the untreated grain increased from 0.11 to 3.73 g KOH kg −1 during three months' storage, whereas the wet thermally treated samples showed no significant increase ( P > 0.05). Peroxide and conjugated diene values of wet thermally treated samples increased substantially, whereas those of flour from untreated grain did not, indicating less formation of rancid final oxidation products in the wet thermally treated samples. Starch degree of cook of the wet thermally treated samples was two times higher than the other treatments. Descriptive sensory evaluation revealed that porridges of flour from untreated grain were associated with hydrolytic rancidity, whereas those of flours from thermally treated grains were not. Consumers showed a preference for the porridges prepared from flour of boiled as well as toasted grain. CONCLUSION: Thermal treatments can be applied to extend whole pearl millet flour shelf‐life, and the treatment of boiling can be used to produce pearl millet flour that cooks more quickly. Copyright © 2008 Society of Chemical Industry
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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".