Using ATR-FTIR spectra and convolutional neural networks for characterizing mixed plastic waste
Using ATR-FTIR spectra and convolutional neural networks for characterizing mixed plastic waste
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DOI：
10.1016/j.compchemeng.2021.107547
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发表时间：
2021-09-24
影响因子：
4.3
通讯作者：
Zavala, Victor M.
中科院分区：
文献类型：
--
作者：
Jiang, Shengli;Xu, Zhuo;Zavala, Victor M.
我们提出了一个卷积神经网络（CNN）框架，用于对混合塑料废物（MPW）流中常见的不同类型的塑料材料进行分类。CNN框架使用实验ATR-FTIR（衰减全反射-傅里叶变换红外光谱）光谱对十种不同的塑料类型进行分类。这种类型的光谱数据的一个重要方面是，它可以被实时收集;因此，这种方法提供了一种途径，使高通量表征MPW。提出的CNN架构（我们称之为PlasticNet）使用光谱的Gramian角表示。我们发现，这种2维（2D）矩阵表示突出了不同频率（波数）之间的相关性，并导致分类精度的显着提高，com-quarantine直接使用频谱（一维矢量表示）。我们还证明了PlasticNet的整体分类准确率可以达到87%以上，并且可以以100%的准确率对某些塑料进行分类。我们的框架还使用显着性图来分析信息量最大的光谱特征。(c)2021爱思唯尔有限公司保留所有权利。
We present a convolutional neural network (CNN) framework for classifying different types of plastic ma-terials that are commonly found in mixed plastic waste (MPW) streams. The CNN framework uses exper-imental ATR-FTIR (attenuated total reflection-Fourier transform infrared spectroscopy) spectra to classify ten different plastic types. An important aspect of this type of spectral data is that it can be collected in real-time; as such, this approach provides an avenue for enabling the high-throughput characterization of MPW. The proposed CNN architecture (which we call PlasticNet) uses a Gramian angular representation of the spectra. We show that this 2-dimensional (2D) matrix representation highlights correlations between different frequencies (wavenumber) and leads to significant improvements in classification accuracy, com-pared to the direct use of spectra (a 1D vector representation). We also demonstrate that PlasticNet can reach an overall classification accuracy of over 87% and can classify certain plastics with 100% accuracy. Our framework also uses saliency maps to analyze spectral features that are most informative. (c) 2021 Elsevier Ltd. All rights reserved.