Effect of Temperature and Time on the Bacterial Community Changes and <i>Enterobacteriaceae</i> Counts Analysis for Shelf Life Estimation of Hainan Tropical Fresh-Cut Fruit Trays
Bibliographic record
Abstract
Storage time and temperature are key factors in the growth of disease-causing and spoilage-causing microorganisms in tropical fresh-cut fruit trays, which affect the shelf life and food safety of fruit trays. The aim of this study was to characterize the bacterial community in tropical fresh-cut fruit trays and to establish a growth model and predict the shelf life of the fruit trays by the change in the number of Enterobacteriaceae bacteria to facilitate the control of storage temperature and time during the trading process. The results showed that Proteobacteria demonstrated significant changes at different storage temperatures conditions (6, 10 and 15°C). Sensory analysis showed a loss in freshness and texture and an increase in ripeness at the three storage temperatures, with shelf life of tropical fresh-cut fruit trays being within 24 hours at 6°C and sold within 10 hours if possible at 10°C. The growth model and shelf-life prediction model with Enterobacteriaceae bacterial population finally yielded a theoretical shelf-life of 7.8 h at 15°C. Based on the results of the above study, fruit retailers can adjust the storage conditions and time of tropical fresh-cut fruit trays to effectively reduce the spoilage rate of fruit trays and contribute to food loss and waste at the consumer and retail levels. Meanwhile, food safety risks can be effectively reduced.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".