Studies on the Gelatinization Temperature of Some Cereal Starches
Bibliographic record
Abstract
Gelatinization temperature of starches from different varieties of cereals, deep-red sorghum, red sorghum, white sorghum, mixed sorghum, white fonio (Digitaria exilis), black fonio (Digitaria iburua), Eleusine coracana (tamba), proso millet (p. miliacium), brown top millet (p. ramosum) and finger millet (Eleusine coracana) were determined by electrical conductivity (EC) method. Among the untreated starches, the results showed that tamba recorded the least onset and peak gelatinization temperatures of 53.0oC and 63.0oC respectively while the highest onset gelatinization temperature, 76oC was recorded with proso millet. Proso millet and finger millet had the highest peak gelatinization temperature. deep-red sorghum and red sorghum had the least end gelatinization temperature while proso millet recorded the highest value. The 0.1MKCl treatment of cereals starches produced elevated onset gelatinization temperatures (62.0-78.0oC). Mixed sorghum had the least onset gelatinization temperature while proso millet and finger millet recorded the highest gelatinization temperature. Following the 0.1MKCl treatment, starches from tamba and proso millet recorded the least and highest peak gelatinization temperatures respectively. Deep-red and red sorghum recorded the least end gelatinization temperatures while proso millet, brown top millet and finger millet recorded the highest end gelatinization temperatures. The treatment of the cereals starches with 0.10MKCl significantly (p < 0.05) delayed the onset gelatinization temperature of the starches and narrowed the gelatinization temperature range. The results of the conductivity of both the 0.1MKCl treated and untreated starch showed nonlinearity within the gelatinization range. This finding has important implication on the energy and time requirement to gelatinize the starch for specific industrial use.
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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".