Variations in nutritional and microbial composition of napa cabbage kimchi during refrigerated storage
Bibliographic record
Abstract
The health benefits of kimchi are proven to be effective against many diseases. We studied the effects of prolonged refrigerated storage on health beneficial qualities of napa cabbage kimchi by assessing variations in nutritional composition, microbial diversity, total antioxidant capacity (TAC), and total polyphenol content (TPC). The carbohydrate content of kimchi drastically reduced during the first month of storage (from 58.7 to 41.6 g/100 g), while fat and ash contents increased. Total bacterial count declined (>80 folds) during 17 months of storage with no development of pathogenic microbes. Microbial diversity did not significantly alter during kimchi storage. Relative abundances of Lactobacillales and Firmicutes increased up to 3 months and Actinobacteria increased after 3 months of storage. TAC and TPC of kimchi increased up to 3 months of storage and declined after. The findings suggest consuming kimchi during the first 3 months of refrigerated storage to optimize beneficial microbes and health benefits. Novelty impact statement Evaluation of variations in health beneficial qualities of napa cabbage kimchi under prolonged refrigerated storage is important to determine the optimum time for consumption. The abundance of beneficial Lactobacillales bacteria increases together with total antioxidant capacity and total polyphenol content in tested kimchi up to 3 months of storage before declining. Tested commercial kimchi can be consumed within 3 months of refrigerated storage for optimum health benefits.
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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 | 0.001 |
| 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".