Enhancing seafood freshness monitoring: Integrating color change of a food-safe on-package colorimetric sensor with mathematical models, microbiological, and chemical analyses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it