Cocoa Mucilage: A Novel Substrate for Fermented Tea-Based Beverages
Bibliographic record
Abstract
Cocoa mucilage, predominantly comprised of sugars, yeasts, and minerals, poses a potential wealth in fermentation processes.This study explores its under-exploited potential by investigating its use as a sugar substitute in the production of a fermented beverage, thereby adding value to what is often dismissed as waste and extending its applications.The research was designed to assess the effects of varying concentrations of cocoa mucilage in the fermentation process of the Symbiotic Culture of Bacteria and Yeast (SCOBY), with a focus on producing a beverage based on green and black tea.The experimental design incorporated factors such as the concentration of mucilage (15, 20, and 30%) and the type of tea (green or black).The process of producing the fermented beverage was meticulously described, followed by comprehensive physicochemical (including pH, brix, alcohol, acidity, among others), microbiological (E.coli, Salmonella, yeasts, and bacteria), and sensory (colour, odour, flavour, sweetness, and astringency) analyses.Six experimental units were formulated by modulating the mucilage concentration and the tea type.The outcomes demonstrated pH values of 3.58, 2.90 ° Brix, 0.136% acidity, a density of 0.98, turbidity of 10.4 NTU, 1.61 ° GL, and the absence of any microbiological contamination.The combination of 20% mucilage concentration with black tea (a1b1) received the highest approval in the sensory analysis, with an average score of 7.09/10.00from 18 testers.In industrial applications, cocoa mucilage could be harnessed as a fermentative source due to the presence of Saccharomyces Servisiae yeast type, which facilitates sugar oxidation in fermented beverages.Thus, this research proposes an alternative use for cocoa mucilage, contributing to waste reduction and broadening its potential applications.
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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 | 0.001 | 0.001 |
| 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".