Impact of NaCl on physicochemical properties, microbial community, and pathogen surveillance in the Chinese traditional fermented broad bean (Vicia faba L.) paste
Bibliographic record
Abstract
Doubanjiang (DBJ), a mixed fermented broad bean (Vicia faba L.) and red pepper (Capsicum annuum L.) condiment used worldwide, is known for its high-salt content (∼20g/100g). Industry demands sodium reduction in DBJs. In this study, we investigated the physicochemical properties, microbial community changes, and pathogen surveillance in DBJ samples with varying salt contents (10g/100g, 15g/100g, 20g/100g and 25g/100g). Our findings revealed that these samples could be categorized into two groups: low-salt (10g/100g) and high-salt samples (15-25g/100g). In low-salt samples, Lactobacillus emerged as the predominant bacteria, exhibiting higher antioxidant activity. The most concentrated organic acids were γ-aminobutyric acid and lactic acid. Conversely, Staphylococcus dominated the bacteria composition in high-salt samples. Human pathogenicity such as Proteus mirabilis, Escherichia coli, and Klebsiella pneumoniae were discovered in both low- and high-salt samples. Higher abundance of antimicrobial resistances was observed in high-salt samples while slightly increased biogenic amine content in low-salt samples. This knowledge provides insights into impact of NaCl on fermented broad bean (Vicia faba L.) paste.
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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 | 0.000 |
| 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".