Enhancing seafood freshness monitoring: Integrating color change of a food-safe on-package colorimetric sensor with mathematical models, microbiological, and chemical analyses
Bibliographic record
Abstract
The study assessed a developed food-safe on-package label as a real-time spoilage indicator for fish fillets. This colorimetric sensor is sensitive to Total Volatile Base Nitrogen (TVB-N) levels, providing a correct indication of fish freshness and spoilage. This study evaluates and predicts the shelf-life and effectiveness of an on-package colorimetric indicator. The sensor, using black rice (BC) dye with polyvinyl alcohol (PVOH), polyethylene glycol (PEG), and citric acid (CA) as binders and crosslinking agents, is applied to PET films. The food-safe pH indicator, prepared via lab-scale flexography printing, is durable in humid environments, making it suitable for practical packaging scenarios. The sensor visibly monitored fish spoilage at 4 °C for 9 days. Quality assessment included tracking ΔRGB (total color difference), chemical (TVB-N, pH), and microbiological analyses. Results indicate that the fish samples are fresh up to 4 days of storage at 4 °C; the total viable count (TVC), Pseudomonas growth, TVB-N contents and pH reached: 5.2 (log CFU/ml), 4.31(log CFU/ml), 26.22 (mg N/100 gr sample) and 7.48, respectively. Integrating colorimetric sensor data with mathematical modeling can predict spoilage trends over time. Integrated system offers a smart approach to accurately predicting shelf-life, aiding in optimizing storage conditions, minimizing food waste, and delivering fresh, high-quality fish products to consumers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".