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 R² of calibration of 0.89, and R² 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.

提出了一种新的基于光子学的多元模式识别技术，用于根据肌肉类型、老化和零售展示期来分离野牛肉样品。该技术使用从可见光到近红外高光谱图像（400-1000 nm）获得的颜色属性来预测野牛肌肉样本的稳定性。实施了无监督和监督分类方法，目的是根据肌肉类型、老化期和零售展示期来区分肌肉样本。从 500 到 690 nm 的波长范围与a发现 CIE Lab 颜色空间中的 * 值对于存储期间肌肉样本的分类非常重要。偏最小二乘判别分析 (PLS-DA) 展示了肌肉类型、老化期和零售展示分类的分类准确度从 0.88 到 0.94，然后是分类图的开发。为的肌肉样品在储存和零售展示期间中的颜色变化的估计，一个*值用的R预测²的0.89校准的，并且R ²的0.88交叉验证。最后，550 到 690 nm 的波长范围可以显着有助于根据肌肉类型、颜色稳定性和储存期对野牛肌肉样品的分类和预测新鲜度。