Storage time effects on the soluble sugars concentration and pH of sweet pearl millet and sweet sorghum juice
Bibliographic record
Abstract
Juice of sweet pearl millet and sweet sorghum can be extracted by mechanical pressing and then fermented to produce bioethanol. Since the juice contains a significant amount of soluble sugars, delays between the juice extraction and its transformation may be critical as sugars can degrade quickly. The main objective of this study was to determine the impact of storage time on sweet pearl millet and sweet sorghum juice pH and soluble sugars concentration. In 2013, the juice extracted from both crop species was stored at ambient temperature or under 4°C in a refrigerator up to 72 h. Juice was sampled every 24 h. In the 2014 experiment, the juice was stored at ambient temperature for 12 h and sampling was made after 0, 2, 4, 7, 8, 10, and 12 h. Obtained results showed that when the juice was refrigerated at 4oC, the pH and soluble sugars concentration remained stable throughout the 72 hours of storage. At ambient temperature, the total soluble sugars concentration significantly decreased during the first 24 h of storage of sweet sorghum juice (-17%) and between the first 24 to 48 h of sweet pearl millet juice (-29%). However, the total soluble sugars concentration did not vary in the juice of both crop species during the first 12 h of storage at ambient temperature. Producers have therefore up to a 12h-storage delay at ambient temperature after extraction before the need of a transformation process or the addition of preservation agents to avoid sugar degradation of the juice of both crop species.
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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 | 0.001 |
| 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".