利用可见/近红外(400～1 000 nm)及近红外(900～1 700 nm)高光谱成像技术对宁夏地区滩寒杂交、 盐池滩羊、 小尾寒羊三个品种羊肉进行识别研究。 针对不同波段光谱特点, 分别优选出Baseline及SG卷积平滑光谱预处理方法, 并运用连续投影算法(SPA)提取特征波长, 结合线性判别分析(LDA)及径向基核函数支持向量机(RBFSVM)模型进行全波段及特征波长识别分析。 结果表明不同波段高光谱对羊肉品种识别均获得较好效果, 其中400～1 000 nm波段采用Baseline-Fullwave-RBFSVM及12个特征波长下准确率为100%与98.75%, 900～1 700 nm波段采用Baseline-Fullwave-RBFSVM及7个特征波长下准确率为96.25%与87.80%; RBFSVM非线性分类准确率高于LDA线性判别结果, 400～1 000 nm波段识别准确率优于900～1 700 nm波段, 说明三种羊肉在色泽纹理上差异比成分含量显著, 利用高光谱成像技术结合RBFSVM方法能够获得较优的羊肉品种识别效果。

This paper focused on the research on identifying and classifying for mutton varieties of Tan-han hybrid sheep,Yanchi Tan-sheep and small-tailed sheep in Ningxia by using visible/ near-infrared (400~1 000 nm). Near infrared (900~1 700 nm) hyperspectral technologies, baseline and SG convolution smoothing spectra pretreatment methods were applied respectively according to the characteristics of different spectrum bands; the characteristic wavelengths were extracted by using successive projection algorithm (SPA);then combined with linear discriminant analysis (LDA) and radial basis kernel function of support vector machine (RBFSVM) model were applied to identify the different mutton varieties under characteristic wavelengths and full-wave bands. Results showed that there were good effects for mutton varieties identification in different hyperspectral bands, among which Baseline-Fullwave-RBFSVM and the same models under 12 characteristic wavelengths obtained accuracy of 100% and 98.75% in 400~1 000 nm respectively, and Baseline-Fullwave-RBFSVM and the same models under 7 characteristic wavelengths obtained accuracy of 96.25% and 87.80% in 900~1 700 nm respectively.The identification accuracy of RBFSVM nonlinear classification was higher than the LDA linear discriminant result, meanwhile the identification accuracy in 400~1 000 nm bands was better than in 1 000~1 700 nm bands, which explained that the differences of color and texture were more significant than the component contents among the 3 varieties mutton. Combined hyperspectral technologies with RBFSVM models can obtain a better recognition effect of mutton varieties.