Department of Systems Design Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
Department of Systems Design Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
Environ Pollut. 2023 Nov 15;337:122548. doi: 10.1016/j.envpol.2023.122548. Epub 2023 Sep 25.
The fast and accurate identification of MPs in environmental samples is essential for the understanding of the fate and transport of MPs in ecosystems. The recognition of MPs in environmental samples by spectral classification using conventional library search routines can be challenging due to the presence of additives, surface modification, and adsorbed contaminants. Further, the thickness of MPs also impacts the shape of spectra when FTIR spectra are collected in transmission mode. To overcome these challenges, PlasticNet, a deep learning convolutional neural network architecture, was developed for enhanced MP recognition. Once trained with 8000 + spectra of virgin plastic, PlasticNet successfully classified 11 types of common plastic with accuracy higher than 95%. The errors in identification as indicated by a confusion matrix were found to be caused by edge effects, molecular similarity of plastics, and the contamination of standards. When PlasticNet was trained with spectra of virgin plastic it showed good performance (92%+) in recognizing spectra that had increased complexity due to the presence of additives and weathering. The re-training of PlasticNet with more complex spectra further enhanced the model's capability to recognize complex spectra. PlasticNet was also able to successfully identify MPs despite variations in spectra caused by variations in MP thickness. When compared with the performance of the library search in identifying MPs in the same complex dataset collected from an environmental sample, PlasticNet achieved comparable performance in identifying PP MPs, but a 17.3% improvement. PlasticNet has the potential to become a standard approach for rapid and accurate automatic recognition of MPs in environmental samples analyzed by FPA FT-IR imaging.
快速准确地识别环境样品中的微塑料对于了解微塑料在生态系统中的归趋和传输至关重要。由于添加剂、表面改性和吸附污染物的存在，使用传统库搜索程序通过光谱分类识别环境样品中的微塑料可能具有挑战性。此外，当在透射模式下采集傅里叶变换红外（FTIR）光谱时，微塑料的厚度也会影响光谱的形状。为了克服这些挑战，开发了一种深度学习卷积神经网络架构 PlasticNet，用于增强微塑料识别。PlasticNet 在经过 8000 多个原始塑料光谱的训练后，成功地对 11 种常见塑料进行了分类，准确率高于 95%。混淆矩阵显示的识别错误是由边缘效应、塑料分子相似性和标准物质污染引起的。当 PlasticNet 用原始塑料光谱进行训练时，它在识别由于添加剂和风化而增加复杂性的光谱时表现出良好的性能（92%+）。用更复杂的光谱重新训练 PlasticNet 进一步增强了模型识别复杂光谱的能力。即使由于微塑料厚度变化导致光谱变化，PlasticNet 也能够成功识别微塑料。与在从环境样品中收集的相同复杂数据集上进行 MPs 识别的库搜索性能相比，PlasticNet 在识别 PP MPs 方面表现相当，但提高了 17.3%。PlasticNet 有可能成为用于快速准确地自动识别通过傅里叶变换衰减全反射（FT-IR）成像分析的环境样品中的 MPs 的标准方法。
