关键词: 高光谱成像技术, 羊肉, BP神经网络, 支持向量机, 嫩度

Abstract: Traditional lamb quality detection has low efficiency and will destroy samples. In order to rapidly and non-destructively test tenderness of chilled fresh lamb, taking Xilingol lamb in Inner Mongolia as research object, the spectrum was corrected by multivariate scattering correction method, and feature images at 620.23, 761.48, 819.48 nm were obtained by principal component analysis method,  and their texture features and color features were extracted to establish BP neural network and support vector machine prediction models for lamb tenderness. The results showed that the prediction effect of the BP neural network model was better than that of the support vector machine model. The determination coefficient (R2) and root mean square error (RMSEP) of the BP neural network model on the prediction set were 0.85 and their 1.86, and those of support vector machine model were 0.77 and 2.37, respectively. Above results showed that it was feasible to use hyperspectral and image information feature layer fusion method to predict the tenderness of cold fresh lamb.

Key words: hyperspectral imaging technology, lamb, BP neural network, support vector machine, tenderness