Recent Progress in Intelligent Packaging for Seafood and Meat Quality Monitoring
Bibliographic record
Abstract
Abstract Food waste plays a crucial role in environmental and human health issues. To address these issues, intelligent packaging systems are proposed for the accurate assessment of the quality of food products. Intelligent packaging is a technology that integrates sensing systems with conventional packaging to provide smart sensing communication functions for real‐time food quality monitoring. The most important parts of this technology are generally classified as quantitative sensors (sensors) and qualitative sensors (indicators). Since seafood and meat products can spoil easily if not stored properly, applying intelligent packaging for real‐time monitoring of the safety of these products is beneficial. In this review, the spoiling process and essential performance characteristics are indicators of the quality of these food products are first discussed. Then, The characteristics and importance of various sensors and indicators that can be used for seafood and meat quality monitoring are presented. While discussing these topics, an updated review of recent scientific studies, preconditions, materials, advantages, and limitations are provided. Furthermore, the future need for improvements in intelligent packaging systems for real‐time quality and safety monitoring of food products is discussed. Finally, several important research examples of the challenges and perspectives of intelligent packaging applications for meat and seafood quality monitoring are presented.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".