AI Article Synopsis
- Determining the freshness of chilled pork is crucial for consumers, but traditional methods are often expensive and time-consuming, making them inaccessible for small traders.
- This study utilized smartphone photography and image analysis to track color changes in pork stored at 4°C for 7 days, achieving high prediction accuracy for freshness indicators.
- The researchers developed a non-destructive and user-friendly method using machine learning models, which outperformed traditional handheld colorimeters in both stability and accuracy.
Article Abstract
Background: Determining the freshness of chilled pork is of paramount importance to consumers worldwide. Established freshness indicators such as total viable count, total volatile basic nitrogen and pH are destructive and time-consuming. Color change in chilled pork is also associated with freshness. However, traditional detection methods using handheld colorimeters are expensive, inconvenient and prone to limitations in accuracy. Substantial progress has been made in methods for pork preservation and freshness evaluation. However, traditional methods often necessitate expensive equipment or specialized expertise, restricting their accessibility to general consumers and small-scale traders. Therefore, developing a user-friendly, rapid and economical method is of particular importance.
Results: This study conducted image analysis of photographs captured by smartphone cameras of chilled pork stored at 4 °C for 7 days. The analysis tracked color changes, which were then used to develop predictive models for freshness indicators. Compared to handheld colorimeters, smartphone image analysis demonstrated superior stability and accuracy in color data acquisition. Machine learning regression models, particularly the random forest and decision tree models, achieved prediction accuracies of more than 80% and 90%, respectively.
Conclusion: Our study provides a feasible and practical non-destructive approach to determining the freshness of chilled pork. © 2024 Society of Chemical Industry.
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| http://dx.doi.org/10.1002/jsfa.13865 | DOI Listing |
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