Effects of Red-Bean Tempeh with Various Strains of Rhizopus on GABA Content and Cortisol Level in Zebrafish
Bibliographic record
Abstract
Tempeh is traditionally produced by fermenting soybean with the fungus Rhizopus oligosporus found in banana leafs. We wanted to investigate if Taiwan’s flavorful red bean could be used as a healthy substitute for soybeans in tempeh. One bioactive component of tempeh is γ-Aminobutyric acid (GABA). We measured GABA content and shelf-life-related antimicrobial activity in red-bean tempeh made with four strains of Rhizopus, one purchased strain of Rhizopus, and an experimental co-cultured group (Rhizopus and Lactobacillus rhamnosus BCRC16000) as well as cortisol in red-bean-tempeh-treated zebrafish. GABA was highest in the co-culture group (19.028 ± 1.831 g kg−1), followed by screened Strain 1, the purchased strain, and screened Strain 4. All strains had antibacterial activity on S. aureus and B. cereus. The extract significantly reduced cortisol in zebrafish. However, Strain 1, with less GABA than some of the other strains, had the best effect on cortisol level, suggesting that other components in red-bean tempeh may also affect stress-related cortisol. We found the benefits of red-bean tempeh to be similar to those reported for soybean-produced tempeh, suggesting that it could be produced as an alternative product. Considering the Taiwanese appreciation of the red-bean flavor, it might find a welcoming market.
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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 | 0.001 |
| 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 | 0.001 |
| 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".