Effects of Varying Grilled Sorghum Content on the Quality Parameters of Urwagwa, a Traditional Rwandese Banana-based Alcoholic Beverage
Bibliographic record
Abstract
An alcoholic beverage called Urwagwa is a common and popular traditional Rwandese drink that is produced from banana juice supplemented with coarsely ground grilled sorghum. Due to limited information on how different sorghum concentration affected the physico-chemical characteristics of this traditional beverage, a study was carried out to investigate how varying this ingredient influenced the quality of the final product. Different levels of grilled coarsely ground sorghum (0%, 2%, 5%, 7%, 10% and 15%) were mixed with banana juice and natural fermentation allowed to take place in six separate fermenters for six days at room temperature (25°C). Four parameters; ethanol content, total soluble solids, pH and titratable acidity were monitored after every 24 hours for 144 hours. Results obtained at the end of fermentation indicated that the ethanol content in all fermenters ranged from 10-12% v/v, total soluble solids 8.1-9.7°Brix, pH 4.0-4.7 and titratable acidity 0.6-0.9%. The highest fermentation rate was observed in fermenters containing ground grilled sorghum compared to the control (0% sorghum added).The highest total soluble solid at the completion of fermentation was found in banana beverage with zero percent of sorghum while the highest pH was generally found in fermenters with sorghum combinations. High titratable acidity was observed in fermenter with zero percent of sorghum. The concentration of 5% of ground grilled sorghum was found to result to the most desirable alcoholic banana beverage as it gave the highest yield of ethanol content and other biochemical parameters, as well as a more desirable flavor. Based on these findings, it could be recommended that the traditional Urwagwa brewers adopt the 5% of sorghum content in the production process in order to obtain a better quality and more acceptable alcoholic beverage.
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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 | 0.002 | 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 | 0.000 | 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".