Rapid Classification and Identification of Plastic Using Laser-Induced Breakdown Spectroscopy With Principal Component Analysis and Support Vector Machine

A large number of discarded plastic products cause serious damage to the ecological environment. It is urgent to recycle plastic by classification. The traditional classification method can not meet the needs of industrial production due to its high cost, low efficiency and complex operation. Laser-induced breakdown spectroscopy (LIBS) has been widely used in the field of substance identification with many advantages, such as simplicity, flexibility, speed and sensitivity. In this paper, 20 kinds of plastics were classified and identified by LIBS combined with principal component analysis (PCA) and support vector machine (SVM). Since few papers have studied the classification and recognition rate of plastic at present, the experiment further studies and analyzes the time spent in the experimental process on the premise of ensuring the accuracy of identification, so as to meet the requirements of rapid classification in industrial production. During the study, 100 groups of spectral data were collected for each plastic, 50 groups of data were randomly selected as the training set to establish the model, and the remaining 50 groups were used as a test set to validate model. Therefore, the training set and the test set each had 1 000 groups of spectral data. The data of the training set was input into SVM for training without any processing, and the best model was established by using the five-fold cross validation. At this time, the recognition accuracy of the test set was 99. 90% , the modeling time was 1 hour, 58 minutes, 41. 13 seconds, and the prediction time was 11. 96 seconds. Thus, it can be seen that the SVM algorithm can be used simply to achieve high accuracy, but it needs a lot of time. In order to improve the experimental efficiency, a principal component analysis algorithm is introduced to process the data, transform the original high-dimensional data into low-dimensional data, and train the model with the data after dimension reduction. For different principal component numbers, the experimental values were obtained by random training ten times and taking the mean value. Experiments show that when the number of principal components is 13, the corresponding recognition accuracy is 99. 80% , while PCA processing time is 1. 44 seconds, modeling time is 12. 16 seconds, and prediction time is only 0. 02 seconds. Although the PCA algorithm combined with the SVM algorithm has a slight decrease in the accuracy of classification and recognition for 20 kinds of plastics, it greatly reduces the time of model training and greatly improves the experimental efficiency. The results show that the two algorithms can be used to classify and identify plastic quickly and accurately.