ArticleNPJ science of food2025
Utilizing drip metabolites and predictive modeling for non-destructive freshness assessment in pork loin.
Article in NPJ science of food, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- The future of meat: innovations in production within an expanding and sustainable food system.Food science of animal resources · 2026Review
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Authors and funding
9 authors.
Funding
Abstract
This study validated the use of pork drip metabolites for non-destructive freshness prediction. The pork loin was vacuum-packaged and stored for 27 days at 4 °C. The pH, drip loss, total aerobic bacterial counts (TAB), microbial composition and drip metabolites were examined. LASSO and Random Forest (RF) were selected and used for variable selection, while Ridge regression and Support Vector Regression were utilized to develop predictive models. Validation was performed using leave-one-out cross-validation. LASSO and RF selected 13 and 10 metabolites, respectively. The metabolites selected by each method were trained using Ridge regression and SVR. Each of the four trained models achieved R
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Registered trials
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