The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification.

中文翻译：

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基于便携式近红外光谱仪和机器学习的消费级塑料快速无损现场分类方法

回收前的塑料废物分类对于实现有效的回收具有重要意义。为了实现快速，无损和现场检测，本研究使用便携式近红外光谱仪获得由ABS，PC，PE，PET，PP，PS制成的标准塑料和商用塑料的漫反射光谱和PVC。在应用了一系列预处理之后，使用主成分分析（PCA）来分析聚类趋势。分别开发和评估了K近邻（KNN），支持向量机（SVM）和反向传播神经网络（BPNN）分类模型。结果表明，经过预处理，可以在顶部三个主要成分空间中很好地分离不同的塑料，分类模型具有优良的分类效果和较高的泛化能力。这项研究表明，便携式近红外光谱仪与化学计量学相结合，可以实现出色的性能，并且在商业塑料识别领域具有巨大的潜力。