Utilizing Coffee Pulp and Mucilage for Producing Alcohol-Based Beverage
Bibliographic record
Abstract
Coffee pulp, mucilage, and beans with mucilage were used to develop alcoholic beverages. The pulp of 45.3% pulp, 54.7% mucilage with seed, and 9.4% mucilage only were obtained during the wet processing of coffee. Musts were prepared for all to TSS (Total soluble solid) 18 °Bx and fermentation was carried out for 12–16 days until TSS decreased to 5 °Bx at 30 °C. Phenolic characteristics, chromatic structures, chemical parameters, and sensory characteristics were analyzed for the prepared alcoholic beverages. Methanol content, ester content, aldehyde, alcohol, total acidity, caffeine, polyphenols, flavonoids, chromatic structure, and hue of the alcoholic beverage from the pulp was 335 mg/L, 70.58 ppm, 9.15 ppm, 8.86 ABV%, 0.41%, 30.94 ppm, 845.7 mg GAE/g dry extract, 440.7 mg QE/g dry extract, 0.41, and 1.71, respectively. An alcoholic beverage from the pulp was found superior to an alcoholic beverage from mucilage with beans and a beverage from mucilage in sensory analysis. There is the possibility of developing fermented alcoholic beverages from coffee pulp and mucilage. However, further research is necessary for quality of the beans that were obtained from the fermentation with the mucilage.
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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) | 0.001 | 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".