|
| Convolutional Neural Network and Support Vector Machine Models for Plastic Classification by Near-infrared Spectroscopy |
| |
| View Full Text Download reader |
| DOI： |
| KeyWord:near-infrared spectroscopy convolutional neural network support vector machine plastic classification |
| Author | Institution |
| ZHANG Wen-jie，JIAO An-ran，TIAN Jing，WANG Xiao-juan，WANG Bin，XU Xiao-xuan |
1. The Key Laboratory of Weak－Light Nonlinear Photonics，Ministry of Education，School of Physics，Nankai University，Tianjin ，China;2. School of Food and Biological Engineering，Jiangsu University，Zhenjiang ，China;3. Ningbo Customs Technology Center，Ningbo ，China |
|
| Hits: 1128 |
| Download times: 884 |
| Abstract: |
| Nowadays it is possible to automatically classify plastic waste by machine learning algorithms， which is of great significance for protecting the natural environment and saving resources. To establish better plastic classification models， the performances of multiplicative scatter correction－support vector machines(MSC－SVM) model and one-dimensional convolutional neural network(1D CNN) model were compared in identifying 4 types of plastic in this paper， as well as the accuracies of NIRS technique for classifying PP new raw material， PP recycled material， PE new raw material and PE recycled material， respectively. Based on the spectra data of 100 plastic samples， the experiment results showed that in validation set， the accuracy for MSC－SVM model is 90.8% while that for 1D CNN model is 91.5%. Particularly， 1D CNN model provided excellent classification results in identifying PE and PP new raw material samples with the accuracies reached up to 100%， which indicated that 1D CNN model is efficient to classify different types of plastic on small dataset. |
| Close |