Rapid Identification of Plastic Beverage Bottles by Using Raman Spectroscopy Combined With Machine Learning Algorithm

Abstract

Rapid and accurate identification of plastic beverage bottles is of great importance because plastic beverage bottles can be encountered as physical evidence in cases involving assaults, thefts, and homicides. In this experiment, 40 commercially available plastic beverage bottles were collected as experimental samples, and their Raman spectral data were collected. Initially, the samples were classified into two categories of polyethylene terephthalate (PET) and polyethylene (PE), and the 35 PET samples were further clustered into three categories by K-means clustering. Savitzky–Golay algorithm smoothing, standard normal variate, multiple scattering correction, and first-order derivatives were utilized to improve the quality of the Raman spectra. A convolutional neural network (CNN) model was constructed for the classification and identification, and four evaluation indexes, such as accuracy, precision, recall, and F1-score, were utilized to compare the model's performance under the four types of preprocessing. The results show that the spectral data preprocessing combining SG and MSC has higher accuracy than other preprocessing methods, and the CNN classification model has the best performance, with 100% correct classification rate in both the training set and the test set, respectively. In conclusion, the results show that convolutional neural networks, when used in combination with Raman spectroscopy, can quickly detect the type of plastic beverage bottle, which is crucial for solving crimes.