Effect of malting regimen on diastatic power, cold and hot water extracts of malts from sorghum
Bibliographic record
Abstract
Summary In this study, sorghum grains were malted using different malting regimens and the malt qualities were analysed for cold water extract (CWE), hot water extract (HWE) and diastatic power (DP). Results showed that malting regimen produced sorghum malts with peak CWE, HWE and DP within the pre‐determined limits of malting regimen. From this, predictive models of sorghum malt quality were developed and tested. Whilst steeping duration interacts significantly with the germination period to cause variations in CWE and HWE, only the germination period significantly influenced the DP of sorghum malts. However, the kilning temperature showed no significant impact on the parameters studied. Besides CWE, HWE and DP correlating positively with each other, the developed predictive models were significant ( P < 0.05) and satisfy the quality of fit ( R 2 > 0.70) of the proposed models. The result demonstrated that wort for different purposes can be obtained by varying the malting operational conditions in the predictive model, thus saving time, resources, preliminary studies and research designs. Besides the promotion of a cheaper local alternative material for industrial wort production and brewing purpose, this research provides valuable insight on the brilliant prospect of variable malting regimen in predicting and adjusting the quality characteristics of sorghum malt and wort. Future studies are required to develop models for wort produced from peak CWE, HWE and DP, for predicting wort quality characteristics.
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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 | 0.001 |
| 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 | 0.001 | 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".