Research on A Classification Algorithm of Near-Infrared Spectroscopy Based on 1D-CNN

Near-infrared (NIR) spectroscopy technology has been widely used in many fields, but spectral pretreatment algorithm easily affects its modelling accuracy. In traditional near-infrared spectroscopy analysis, the selection of pretreatment methods mainly depends on human experience, and sometimes some spectral information will be lost. Therefore, a near-infrared spectrum classification method of a one-dimensional convolution neural network (1D-CNN) without spectral pretreatment is proposed in this paper. In order to compare the classification effects of three traditional near-infrared spectral analysis models of BP neural network (BP), support vector machines ( SVM) and extreme learning machine (ELM) with one-dimensional convolutional neural network (1D-CNN) modeling method, comparative experiments were carried out on NIR data sets of different grades of drug, beer, mango and grape. The experimental results show that the classification accuracy of the 1D-CNN model is the highest, among which the accuracy of drug 4 classification is 96. 77% beer 2 classification is 93. 75% mango 10 classification is 96. 45% and grape 19 classification is 88. 75%. Finally, the effects of seven different spectral pretreatment methods, such as mean centralization (MC), standardization, multiple scatter correction (MSC), standard normal variable transformation (SNV), first-order difference, second-order difference and wavelet transform (WT) on different models are also discussed. After pretreatment, the classification accuracy of the BP neural network, SVM and ELM changes significantly, while the classification effect of the 1D-CNN model has no change before and after pretreatment, and the classification accuracy is still the highest. The results show that compared with the traditional NIR spectral classification methods, the 1D-CNN method proposed in this paper can realize the rapid and accurate NIR classification of food and drugs and does not need any spectral pretreatment. It shows that the deep learning method has broad application prospects and research value in NIR spectral processing.