基于近红外光谱的核桃仁品种快速分类方法

采用傅里叶变换近红外光谱仪，采集了4个不同品种的200份核桃仁样本的近红外漫反射光谱，建立了核桃仁品种分类模型。光谱范围为3 800~9 600 cm -1 ，预处理方法采用多元散射校正法和标准正态化方法；通过主成分分析法优选出5个主成分因子，光谱信息累计贡献率达到99.21%；采用随机抽取法建立建模集和验证集，以主成分因子为输入变量，建立了基于支持向量机分类模型，并采用网格搜索法对RBF核函数参数 λ和δ 进行寻优。分析结果表明，建立的核桃仁分类识别模型对4个核桃仁品种的总体正确识别率达到96%，为核桃仁品种的快速无损识别提供了一种可行的方法。

Rapid Classification Method of Walnut Kernel Varieties Based on Near-infrared Diffuse Reflectance Spectra

Walnut is an important dry fruit and woody oil crop in China, and it has significant meaning to establish a rapid, nondestructive testing method for identification and classification of walnut kernel varieties in walnut processing industry. Near-infrared diffuse reflection spectroscopies of 200 walnut samples of four species were adopted to establish models for rapid and nondestructive classification. The spectral region of walnut samples was ranged from 3 800 cm -1 to 9 600 cm -1 . The spectra data of walnut were processed using the multiplicative scatter correction (MSC) and the standard normalized variate (SNV) methods. Principal component analysis (PCA) was used to reduce the dimensionality of spectra data. The cumulative contribution rate of the first five main components reached 99.21%, which were selected as variables for modeling. Totally 100 walnut samples were selected as training set by random sampling method. The NIR classification model of walnut kernel varieties was built based on support vector machine (SVM) method, and grid search method was used for searching the best parameter. The built model was tested by the rest 100 walnut samples of four species, and the results showed that the correct recognition rate of the model reached 96%. The analyzed results indicated that the NIR classification model could provide a feasible method for rapid and nondestructive identification of walnut kernel varieties.