Intelligent control for improving the quality of fresh meat products with rich lipid in cold chain logistics: research status, challenge, and potential applications
Bibliographic record
Abstract
Lipid-rich fresh meat products are popular among consumers due to their abundant nutrients. However, traditional cold chain logistics struggles to effectively control the quality of such products, leading to a large number of food waste and safety incidents. Novel quality control technologies (including emerging preservation technologies and intelligent detection technologies) provide promising solutions to these challenges, and contribute to intelligent control for improving the quality of lipid-rich fresh meat products in cold chain logistics. Through a comprehensive analysis of the existing literature, this review summarizes the mechanisms leading to quality deterioration in lipid-rich fresh meat products in cold chain logistics, and focuses on the research status of novel quality control technologies. Moreover, this paper discusses the challenges in practical applications of these technologies, as well as the potential applications of 3D printing technology, electrospinning technology and artificial intelligence technology in promoting the intellectualization of quality control technologies. This review aims to provide guidance for the application of novel quality control technologies in lipid-rich fresh meat products, and assist in building an efficient and intelligent system to improve the quality of lipid-rich fresh meat products in cold chain logistics.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".