Research On Pattern Recognition Problems In Non-destructive Testing Of Agricultural Products

Pattern recognition is critical for nondestructive detection of agricultural products and development of high precision agricultural nondestructive detection equipment. In this paper, focusing on pattern recognition problems such as nonlinear data dimension reduction and efficient modeling in the nondestructive detection of agricultural products, three pattern recognition methods including a near infrared spectroscopy dimension reduction method based on autoencoder network manifold learning, an image and spectral classification modeling method based on sparse representation, and a near infrared spectroscopy quantitative modeling method based on relevance vector machine, were researched and applied to nondestructive detection of agricultural products. The main research contents and results are as follows:1. Research on near infrared spectroscopy dimension reduction method based on autoencoder network manifold learning. Autoencoder network (AN) is a nonlinear dimension reduction manifold learning algorithm which can find out nonlinear low-dimensional manifold structure from high dimensional spectra data effectively. In this research, a nonlinear infrared (IR) spectra modeling method AN-PLS was proposed by combining AN and partial least squares (PLS) to reflect the nonlinear correlations existing between IR spectra and physicochemical properties of samples. In AN-PLS, AN and PLS were adopted to deduct the dimensions of IR spectra and build regression calibration model, respectively. The AN-PLS was then applied to correlate the near infrared (NIR) spectra and the mid infrared (MIR) spectra with the concentrations of insoluble dietary fiber in bamboo shoots. The results indicate that AN-PLS can predict the concentrations of insoluble dietary fiber in bamboo shoots with a lower cross validation RMS error (RMSECV) and higher determinative coefficient (R2), than other common spectra data preprocessing methods (such as MSC, SNV, Savitzky-Golay and PCA) combined with PLS or sole PLS. It can be concluded that AN-PLS can effectively model the nonlinear correlations between IR spectra and physicochemical properties of the samples. And it is feasible to accurately detect the concentrations of insoluble dietary fiber in the bamboo shoots by coupling NIR and MIR spectra with AN-PLS modeling method.2. Research on image and spectral identification method based on sparse representation. An identification method based on sparse representation was proposed for nondestructive detection of agricultural products using image and spectral technology. In sparse representation, the identification problem was reduced to the problem as how to represent the testing samples from the data dictionary (training samples data). The identification result thus could be achieved by solving the L-1 norm-based optimization problem. We compared the effectiveness of the proposed method with linear discriminant analysis (LDA) and least squares support vector machine (LS-SVM) by appling it to classification of raisin qulity and recognition of Atlantic salmon flesh color. Experimental results demonstrated that (1) the overall identification accuracy of the proposed method for four raisin qulity varieties images was significantly higher than that of LS-SVM, (2) the identification accuracy of the proposed method for two Atlantic salmon flesh color varieties spectrals was 73%, which was higher than that of LDA (72%) or LS-SVM (68%). Therefore, the proposed method can provide a new effective method for identification modeling for nondestructive detection of agricultural products using image or spectral technology.3. Research on near infrared spectra modeling method based on relevance vector machine. A modeling method based on relevance vector machine (RVM) was proposed for nondestructive detection of agricultural products using spectral technology. The relevance vector machine method which with a high sparse solution and without estimating the regularization parameters, can improve the prediction speed. We compared the effectiveness of RVM method with partial least squares (PLS) and least squares support vector machine (LS-SVM) by appling it to analysis adenosine content in fermentation cordyceps powder. Experimental results demonstrated that prediction accuracy of the proposed method for adenosine content in fermentation cordyceps powder was significantly higher than that of PLS and LS-SVM.