关键词:  , 近红外光谱技术, 三文鱼, 稀疏表示, 最小二乘支持向量机

Abstract:  Salmon meat is of the important indicators of quality to evaluate its merits, if they can accurately distinguish the characteristics of the meat, this can greatly reduce the discrimination time and increase breeding success rate. In this paper, using near-infrared spectroscopy and sparse representation, we can analyze the salmon meat specialties and classify research. If the astaxanthin was used as an index to meat specialties, we can compare the principal component analysis (PCA) and the sparse representation of data in two different spectral dimensionality reduction method to process it, in the spectral data dimensionality reduction, we are able to establish classification based on linear discriminant the classification algorithm analysis (LDA) and least squares support vector machine(LS-SVM) classification. The test results show that the sparse representation model correct classification rate and reduce the dimension accuracy rate are higher than the principal component analysis. Therefore, the sparse representation classification provides a new effective way for meat classification.

Key words: near infrared spectroscopy, salmon, sparse representation, least squares support vector machine