Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis

Abstract

The potential of multi-spectral visible-near infrared imaging to discriminate beef meat muscles in relation with their type and animal origin was examined in the present study. Two hundred forty muscles of three types (longissimus thoracis, biceps femoris, and semimembranosus) were obtained from the carcasses of three types of animals, two late-maturing cattle types of animals (Limousin and Blond d’Aquitaine) that grow slowly and deposit more muscles and less fat, compared to one early-maturing cattle types of animals (Angus) which tends to have muscles richer in collagen and in intramuscular fat. Two hundred forty cube images were collected with nineteen Ligth Emitting Diodes (405 to 1050 nm) using the Videometer Lab2 device. The image cubes were processed in order to extract image mean spectra and image shape features from co-occurrence and difference of histogram matrices. The results of the partial least square discriminant analysis performed on image texture features and spectral data show a maximum ranging from 63.5 to 83% of good classification depending on the muscle and breed considered. This study demonstrated the promising potential of the visible-near infrared multi-spectral imager to characterize beef meat muscles based on muscle type and its animal origin.