Article 2026 · 0 atıf

Study on methods for measuring beef color and predicting storage time based on computer vision

Chen, Yixuan, Zhou, Jinghao, Oz, Fatih, He, Zhiyong, Wang, Zhaojun, Chen, Qiuming, Zeng, Maomao, Chen, Jie

Özet

In this study, a non-destructive and accurate method based on computer vision and machine learning was developed to measure beef surface color and to predict storage time and the relative proportion of oxymyoglobin. Images of the Longissimus thoracis (LT) muscle were acquired with a camera, and the red muscle region was segmented by integrating GrabCut and Otsu binarization techniques to extract RGB values, which were subsequently converted to CIE L*, a* and b* color space values. Colorimetric characteristics derived from computer vision were compared with those obtained from a traditional colorimeter, and the results revealed higher sensitivity to temporal changes and can provide a more representative reflection of the actual appearance of beef. A convolutional neural network (CNN) model trained by images derived from computer vision achieved R2 values of 0.926 and 0.893 for storage time and oxymyoglobin content prediction, respectively, thereby providing an efficient and scientifically robust framework for assessing beef freshness.

Anahtar Kelimeler

Computer vision Beef color Myoglobin measurement Convolutional neural network DECISION TREES MEAT MYOGLOBIN CLASSIFICATION

WoS Kategorileri

Food Science & Technology

Künye

Dergi: MEAT SCIENCE
Yıl: 2026
Cilt: 234
ISSN: 0309-1740
eISSN: 1873-4138
DOI: 10.1016/j.meatsci.2026.110044
WoS UID: WOS:001679993600001

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