基于近红外光谱技术的三文鱼肉质分类研究  

Meat Classification of Salmon Based on Near Infrared Spectroscopy and Sparse Representation

作　　者：王磊[1] 殷姣姣[1] 余心杰[2] 

机构地区：[1]太原科技大学电子信息工程学院,山西太原030024 [2]浙江大学宁波理工学院信息科学与工程学院,浙江宁波315100

出　　处：《计算机与现代化》2015年第9期109-112,共4页Computer and Modernization

基　　金：国家自然科学基金资助项目(31201446);浙江省自然科学基金资助项目(LY15C190011);宁波市民生科技基金资助项目(2013C11026)

摘　　要：三文鱼的肉质是评价其品质优劣的重要指标,如果能精确地分辨出其肉质特色,可大大缩减判别时间,增加养殖成功率。本文采用近红外光谱技术和稀疏表示,分析三文鱼的肉质特色,并对其进行分类研究。以虾青素作为肉质特色的分类指标,比较主成分分析法(PCA)和稀疏表示2种不同的光谱数据降维方法对其进行处理,在光谱数据降维的基础上,采用基于线性判别分析的分类算法(LDA)和基于最小二乘支持向量机算法(LS-SVM)建立分类模型。实验结果表明,稀疏表示降维处理的分类模型正确率和准确率要高于主成分分析法。因此,该算法对肉质分类提供了一种新的有效的途径。Salmon meat is of the important indicators of quality to evaluate its merits, if they can accurately distinguish the char-acteristics 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.

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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]