Bison muscle discrimination and color stability prediction using near-infrared hyperspectral imaging

A novel photonics-based multivariate pattern recognition technique is presented to segregate bison meat samples based on muscle type, ageing, and retail display period. The technique uses color attributes obtained from visible to near-infrared hyperspectral images (400-1000 nm) to predict the stability of bison muscle samples. Unsupervised and supervised classification methods were implemented with an aim to discriminate muscle samples based on muscle type, ageing period, and retail display period. The wavelength region from 500 to 690 nm which is associated with the a* value in the CIE Lab color space was found to be significantly important for the classification of muscle samples over the storage period. Partial least squares discriminant analysis (PLS-DA) demonstrated classification accuracies from 0.88 to 0.94 for the classification of muscle type, ageing period and retail display followed by development of classification maps. For the estimation of color changes in the muscle samples over the storage and retail display period, a* value was predicted with an R-2 of calibration of 0.89, and R-2 of cross-validation of 0.88. Conclusively, the wavelength range from 550 to 690 nm can significantly contribute to sorting and predicting freshness of bison muscle samples based on muscle type, color stability and storage period. (C) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated summary

A novel photonics-based multivariate pattern recognition technique is used to segregate bison meat samples based on color attributes obtained from visible to near-infrared hyperspectral images. The wavelength region from 500 to 690 nm plays a significant role in classification, with Partial Least Squares Discriminant Analysis demonstrating high accuracy.