Determination of Optimum Temperature for Amount Lactic Acid Bacteria and Antioxidant Activity from Pickled Bamboo Betung Boobs (Dendrocalamus asper)
Bibliographic record
Abstract
Pickled betung bamboo shoots (Dendrocalamus asper) are a traditionally fermented product and can potentially be a source of probiotics. Pickled bamboo shoots contain good microorganisms, namely LAB, which are bacteria that contribute to the food sector for natural fermentation. Fermentation of bamboo shoots produces antioxidant compounds at an optimum temperature range. The research aims to understand the optimum temperature for the amount of LAB and antioxidant activity in pickled betung bamboo shoots. This research is a laboratory experiment with 3 treatments, namely 100 grams of bamboo shoots fermented with pickled bamboo shoots at a temperature of 15℃ (K1), a temperature of 37℃ (K2), and a temperature of 40℃ (K3). The three treatments were incubated for 72 hours with 4 repetitions. The research stages included processing pickled bamboo shoots, isolating bacteria using the dilution series-pour plate method, and antioxidant testing using the DPPH method. The number of LAB is calculated using the Quebec Colony Counter. Data analysis used Anova and Duncan's test. The results obtained include that temperature influences the amount of BAL in pickled bamboo shoots with a value of p=0.016. The number of LAB at a temperature of 150C was 59.50, at a temperature of 37 129.74, and a temperature of 40 107.57. Temperature also affects antioxidant activity. The average values at temperatures of 15.37, 40℃ are 30.60%, 25.241%, and 16.8782% (% inhibition), 36.3655, 41.6775, 51.0757 (IC50). The optimum temperature for the number of BAL pickled bamboo shoots is 37°C, and the optimum temperature for the number of antioxidants is 15°C.
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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) | 0.001 | 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".