基于DPLS特征提取的LDA方法在玉米近红外光谱定性分析中的应用

Application of DPLS-Based LDA in Corn Qualitative Near Infrared Spectroscopy Analysis

提出了一种基于DPLS+LDA的玉米近红外光谱定性分析新方法。该方法在训练时,首先用包含30个玉米品种每个品种20个近红外光谱样本的训练集进行DPLS回归,确定最佳DPLS主成分数为28;然后对训练集光谱进行DPLS特征提取后再进行LDA分析,确定最佳LDA主成分数为26,并提取LDA特征。识别时,测试样本经过DPLS+LDA特征提取后,用最小距离分类器进行识别。实验比较了DPLS+LDA方法与传统的DPLS回归预测方法及DPLS特征提取方法的判别结果,DPLS+LDA方法的性能最优,等识率达到了96.18%,而传统DPLS预测方法只有85.38%,DPLS特征提取方法为95.76%。实验结果说明DPLS+LDA方法是一种有效的玉米近红外光谱定性分析方法,且具有很强的推广能力。

NIR technology is a rapid,nondestructive and user-friendly method ideally suited for Qualitative analysis.In this paper the authors present the use of discriminant partial least Squares（DPLS）-based linear discriminant analysis（LDA） in corn qualitative near infrared spectroscopy analysis.Firstly,a training set including 30 corn varieties（each variety has 20 samples） was used to build the DPLS regression model,and 28 principal components（DPLS-PCs） were obtained from original spectrum.Secondly,the DPLS-PCs scores of the training set were extracted as DPLS features.Thirdly,LDA was applied to the DPLS features,determining 26 principal components（LDA-PCs）.A test sample was first projected onto the DPLS-PCs and then onto the LDA-PCs,and finally 26 DPLS＋LDA features were obtained.The recognition results were obtained by minimum distance classifier.DPLS＋LDA method achieved 96.18% recognition rate,while traditional DPLS regression method and DPLS feature extraction method only achieved 85.38% and 95.76% recognition rate respectively.The experiment results indicated that DPLS＋LDA method is with better generalization ability compared with traditional DPLS regression method and NIRS analysis by DPLS＋LDA method is an efficient way to discriminate corn species.