Machine Learning Approaches to Understanding Meat Color Changes via Digital Imaging

Ensuring meat quality is critical for consumer satisfaction and food safety, but monitoring subtle quality changes remains a challenge. This study presents a smartphone-based imaging approach combined with advanced data analysis techniques to evaluate color changes in red meat under different storage conditions. Meat samples stored in a refrigerator (۴°C) and a freezer (-۱۹°C) were analyzed over three weeks using RGB and HSV color spaces. Principal Component Analysis (PCA) revealed patterns of color change, while ANOVA-Simultaneous Component Analysis (ASCA) identified significant effects of storage time and temperature on meat color, with the HSV color space showing greater sensitivity. Partial Least Squares Discriminant Analysis (PLS-DA) successfully classified chilled and frozen samples after temperature equilibration, with the soft classification method demonstrating robust performance. These results highlight the potential of integrating accessible imaging tools and machine learning techniques for objective and efficient meat quality assessment, providing a scalable solution for the food industry.

کلیدواژه ها:

Machine Learning ، Meat ، ASCA ، PCA ، PLS-DA ، Digital imaging