Analysis of the Nutrients and Microbiological Characteristics of the Indonesian Dadih As a Food Supplementation
Bibliographic record
Abstract
Dadih, an Indonesia traditional fermented buffalo milk, is produced and consumed by the West Sumatra Minangkabau ethnic group of Indonesia that considered beneficial for human health. The objective of this study was to know nutrients composition and bacteriology characteristics of dadih that collected from Tanah Datar and Agam districs in West Sumatera province, Indonesia. This study initiated with analysis of biochemical of dadih covering protein, lipid, moisture value, ash content, pH, and titritable acidity. Bacteriology analysis have conducted to total bacterial and total Acid Lactic Bacterial quantification. In this study, we have found nutrients compositions of dadih are total percentage of protein, lipid, moisture value, ash content, pH, and titritable acidity of dadih from Tanah Datar respectively are 12.41±1.30, 5.70±1.73, 66.09±6.00, 0.72±0.13, 4.55±0.21, 0.51±0.56. Total percentage of protein, lipid, moisture value, ash content, pH, and titritable acidity of dadih from Agam respectively are 10.89±2.55, 18.00±14.65, 61.94±20.18, 1.14±0.79, 4.33±0.46, 1.70±0.21. Dadih from Tanah Datar contain 1.9 x 107 CFU/g BAL and 2.3 x 107 CFU/g total bacteria. Dadih from Agam contain 4.6 x 106 CFU/g BAL and 2.9 x 108 CFU/g total bacteria. There is not pathogenic bacteria in Dadiah Tanah Datar and Agam.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".