Determination of salmon freshness by computer vision based on eye color. (December 2022)

• Record Type: Journal Article
• Authors: Jia, Zhixin; Li, Meng; Shi, Ce; Zhang, Jiaran; Yang, Xinting
• Is Part Of: Food packaging and shelf life. Volume 34(2022)
• Publication Date: 2022-12
• Subjects: Computer vision·Salmon·Eye color parameters·Chilled storage·Freshness indicators; Food -- Packaging -- Periodicals; Food -- Preservation -- Periodicals; Food -- Packaging; Periodicals; 664.0905
• DOI: 10.1016/j.fpsl.2022.100984
• Languages: English
• ISSNs: 2214-2894

Abstract

Abstract: To evaluate and predict the freshness of salmon nondestructively, a computer vision technology was developed based on eye color to predict multiple freshness indicators of salmon simultaneously during storage at 0 °C. The RGB, L*a*b* , and HSI color spaces of eye images were analyzed by an image processing algorithm. It is demonstrated that the eye color parameters R, G, B, L* , I, and ΔE were correlated with freshness indicators to establish the multiple linear regression (MLR) and support vector regression (SVR) models. The MLR models outperformed SVR models with high correlation coefficients R 2, F value, and low relative errors. The achieved results showed that it was a nondestructive, fast method for predicting the freshness of salmon stored at 0 °C by evaluating the eye color parameters with computer vision. Highlights: Eye color features were utilized to detect salmon trout fish freshness. Color features and freshness indicators were used to build the multiple regression model. Model achieved high accuracy in predicting the freshness of salmon. Nondestructive, reliable, fast method for real-time freshness monitoring of salmon.