Changes in microbial communities and their correlation with physicochemical properties during the fermentation of doenjang
Bibliographic record
Abstract
Despite extensive research on doenjang fermentation, studies exploring the correlation between microbial dynamics and its quality attributes remain limited. This study was conducted to elucidate the relationship between microbial changes and physicochemical characteristics during the fermentation process of doenjang. During the fermentation period of doenjang, from day 0 to day 90, significant decreases were observed in moisture, pH, salinity, and monosaccharide levels such as glucose and fructose, while the contents of amino-type nitrogen (AN) and lactic acid increased. Analysis of microbial changes during the fermentation period of doenjang revealed that the proportions of Bacillus and Tetragenococcus increased as fermentation progressed, whereas the proportions of lactic acid bacteria, such as Enterococcus, Pediococcus, and Leuconostoc, decreased. Bacillus and Tetragenococcus exhibited similar correlation patterns with other variables. The microbial proportions of Bacillus and Tetragenococcus were positively correlated with AN and lactic acid content but negatively correlated with moisture, pH, salinity, and glucose content in doenjang. In contrast, the proportions of lactic acid bacteria such as Enterococcus, Pediococcus and Leuconostoc were negatively correlated with AN and lactic acid content, while showing positive correlations with moisture content, pH, salinity, and glucose content. These findings indicate a close correlation between microbial communities and quality characteristics during the fermentation of doenjang.
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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 | 0.001 |
| 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) | 0.000 | 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".