基于ELM和可见/近红外光谱的鲜枣动态分类检测

枣营养丰富且品种繁多，不同品种的内外部品质与市场价格均存在差异，为了实现鲜枣品种分类的快速无损检测，以产自同一地区的板枣、鸡心枣和相枣为研究对象，动态采集光谱数据。采用移动平滑(moving smoothing)与多元散射校正(multiplicative scatter correction, MSC)相结合的方法预处理光谱数据，对预处理后的光谱数据采用连续投影算法(successive projections algorithm, SPA)提取出11个特征波长分别为：980, 1 860, 1 341, 1 386, 2 096, 1 831, 1 910, 1 628, 441, 768, 601 nm，其重要程度依次递减。以所提取的特征波长作为输入变量，建立极限学习机(extreme learning machine，ELM)分类模型，进行预测判别，并与偏最小二乘判别分析(partial least squares discriminant analysis, PLS-DA)和最小二乘支持向量机(least squares support vector machines, LS-SVM)方法进行比较。结果表明：SPA-ELM方法所建校正模型的决定系数R2=0.972 38，校正均方根误差RMESC=0.018 724，SPA-ELM方法与SPA-PLS-DA和SPA-LS-SVM方法判别准确率均为100%，说明ELM是一种有效的分类判别方法。该研究为鲜枣品种分类检测提供了新的理论基础。

Dynamic Detection of Fresh Jujube Based on ELM And Visible/Near Inf rared Spectra

Jujube was rich in nutrition and variety. In different varieties, there were very different from the market price to the qualities of internal and external. In order to realize the rapid and non-destructive detection of fresh jujubes’ classification, Ban jujube, Jixin jujube and Xiang jujube were selected as research objects to collect their visible/near infrared spectral data dynamically. A combination of Moving Smoothing and Multiplicative Scatter Correction (MSC) was applied as the pretreatment method. After the pretreatment, the characteristic wavelengths extracted by Successive Projections Algorithm (SPA) were 980 nm, 1 860, 1 341, 1 386, 2 096, 1 831, 1 910, 1 628, 441, 768 and 601 nm, respectively. And the importance reduced in accordance with the order. The 11 characteristic wavelengths were adopted as input variable to established Extreme Learning Machine (ELM) classification model, which was used for prediction. Comparing the ELM model’s classification accuracy with other methods’ classification accuracy such as Partial Least Squares Discriminant Analysis (PLS-DA) and Least Squares Support Vector Machines (LS-SVM), the result indicated that: the R2 and the RMSEC of the SPA-ELM model was 0.972 38 and 0.0187 24, respectively. The classification accuracy of the SPA-ELM model was 100% as good as the SPA-PLS-DA and SPA-LS-SVM. ELM was an effective classification method. This study provides a new theoretical basis for detection of fresh jujubes’ classification.