Development of intelligent packaging for real-time monitoring of the freshness of Canadian fish (tilapia and salmon) and pork during storage
Bibliographic record
Abstract
In the present work, the freshness of fish fillets and pork was monitored in real time with intelligent packaging that utilised pH-sensitive food dye strips as indicators of freshness. Strips were attached to the inner sections of transparent plastic lids where the meat samples were stored. pH-sensitive dyes interacted with compounds such as ammonia, dimethylamine, and trimethylamine, collectively referred to as total volatile basic nitrogen, which are released by a deteriorating meat sample into the headspace of the packaging. Deterioration of the Canadian-based pork samples was observed at room temperature (25°C), and all pH strips indicated colour change. For the fish sample, phenol red and bromocresol purple dye indicators were tested. The phenol red dye strip worked best as a colorimetric indicator for monitoring freshness. The phenol red dye strip changed from yellow to a more noticeable red colour when compared to bromocresol purple. For the pork sample, four dyes were compared: bromocresol green, phenol red, methyl red, and bromothymol blue. Bromocresol green was the most reactive of all the dye strips. To further validate the reactivity of the dye strips to deterioration, total viable counts and Pseudomonas spp. counts were determined. The results showed a positive correlation between microbial load and colour change in dye strips within a 60-h period. The total viable count ranged from log 7.59 - 9.8 CFU/g, while the Pseudomonas spp. count ranged from log 6.93 - 10.15 CFU/g. Overall, this method would be an inexpensive approach to food packaging that will benefit the meat industries for monitoring the shelf life of meat samples, thereby increasing consumer confidence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".