AI-based modified atmosphere packaging for fruits and vegetables preservation: research progress and prospects
Bibliographic record
Abstract
Modified atmosphere packaging (MAP) has been extensively applied in the preservation of fruits and vegetables (F&Vs). However, challenges arise from the variety of packaging materials, complex gas compositions, and diverse respiration patterns of F&Vs. Traditional mathematical tools struggle to accurately design, detect, monitor, and predict MAP for foods. Artificial intelligence (AI), as one of the versatile tools which could simulate, extend, and expand human intelligence, has demonstrated its role in many fields, as well as in MAP for F&Vs. This review first revealed the literature and research team overview of AI in the field of MAP for F&Vs through bibliometric analysis. Then, the respective classifications and joint applications of MAP and AI for F&Vs were reviewed. At present, the application of AI-based MAP in exploring mechanisms, packaging design, parameter optimization, quality monitoring, and shelf-life prediction for F&Vs has made preliminary progress. In the future, it needs to develop toward high precision, high throughput, automation, and cost-effectiveness. Meanwhile, challenges remain, including data scarcity, complex models, high learning costs, and difficulties in verifying the authenticity of AI outputs. Additionally, specific issues related to F&V MAP, such as sample variability, packaging material selectivity, and interactions between gases, samples, and films, need further attention.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".