Color Change Analysis of Raw Chicken Breast over Different Timestamps Using CNNs

In this study, the potential of a novel method based on an artificial neural network was investigated to analyze the color change of raw chicken breasts during refrigeration. As edible meat for people, chicken breasts have high nutrients and low‐fat content. Therefore, people consume it as a safe and high‐value food in their daily diet. The investigation of chicken breast freshness is proposed as a significant issue in the meat industry because raw meat spills rapidly. The color change of raw chicken breasts over time reflects subtle biochemical changes, as well as changes in freshness. However, owing to the impact of the photographic equipment and illumination, a significant color discrepancy exists between the captured image and the intrinsic color. It is difficult to separate chicken breast color changes from color discrepancies when comparing images captured at different timestamps. Thus, we propose a color change analysis method for raw chicken breast that uses color correction to suppress the influence of color discrepancy and uses CNN to extract discriminant features for timestamp classification analysis. The experimental results indicate that the proposed method improves all three CNN models by achieving higher accuracy, and the best model was improved from 80.05%$$ 80.05\% $$ to 95.66%$$ 95.66\% $$, demonstrating the effectiveness of the proposed method. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.