Effect of Time, Temperature and Size Reduction on Some Physico-chemical Characteristics of Sorghum bicolour Leaf Sheath Extracts
Bibliographic record
Abstract
Sorghum bicolor leaf sheaths are a common dried ingredient used as a colourant for waakye, a popular Ghanaian dish made from rice and cowpea. The leaf sheaths are also used in traditional medicine due to its impressive bioactive composition. Its potential as a natural food colourant and antioxidant has been established but the effect of different processing conditions on the physicochemical composition and characteristics have not been adequately researched. The present study assessed the effect of size reduction (whole, coarse and fine), temperature (room temperature (28°C) and 98°C) and time (20, 40 and 60 min) on the colour, pH and ascorbic acid content of sorghum leaf sheaths. Samples were steeped in a measured amount of water and analysis conducted on the extracts. Size reduction significantly reduced the lightness (l*) and colour intensity (chroma and hue) of extracts steeped at both temperatures. The pH of all extracts was relatively neutral, ranging from 6.63 to 7.23 and was not significantly affected by size reduction or time. Extraction of ascorbic acid was more effective at 98°C and did not degrade with constant heating within the experimental time. Average ascorbic acid content of extracts was 3.89 g/L. For effective utilization and value addition of Sorghum bicolor leaf sheaths, food producers should consider fine milling and late incorporation into food for optimum colour and phytochemical content preservation.
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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".