Effect of Pineapple Peel on Enzyme Production and Antioxidant Potential in Scoby Fermentation
Bibliographic record
Abstract
The study demonstrated that pineapple peel supplementation enhances microbial activity and enzyme production in kombucha fermentation, with the Symbiotic Culture of Bacteria and Yeast (SCOBY) brewing solution exhibiting a rapid pH decline to 2.96 within 7 days due to organic acid production from acetic acid bacteria and yeasts, ensuring a safe fermentation environment (pH ≤ 4.6). Pineapple peels sustained microbial populations such as yeast, lactic acid bacteria (LAB) and acetic acid bacteria (AAB) at 37 °C, but at 45 °C it caused severe inhibition due to thermal stress, while acting as nutrient sources to support higher cell counts (AAB: 2,620 CFU/mL) compared to controls. This study evaluated protease, cellulase, and lipase activities at 37 °C and 45 °C using agar plate hydrolysis assays. Protease exhibited activity, with hydrolysis indices of 2.2-2.3 at 37 °C and 3.0 at 45 °C, demonstrating temperature-enhanced performance consistent with bromelain’s thermal optima. Cellulase showed peak activity at 37 °C (index 2.4, day 3), declining thereafter, while 45 °C sustained higher indices (2.1-3.3), indicating thermal stability. Lipase displayed moderate activity at 37 °C (index 2.5, day 6) but improved at 45 °C (index 2.7, day 3), aligning with prior reports on thermophilic activation. The results highlight pineapple peels as an effective substrate for thermostable protease and cellulase production, with potential applications in bioconversion processes.
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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 | 0.001 | 0.000 |
| 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".