Buaruk Suphachok, Somnuake Pattara, Gulyanon Sarun, Deepaisarn Somrudee, Laitrakun Seksan, Opaprakasit Pakorn
Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12120, Thailand.
College of Interdisciplinary Studies, Thammasat University, Pathum Thani, 12120, Thailand.
Sci Rep. 2024 Aug 31;14(1):20297. doi: 10.1038/s41598-024-70407-5.
Microplastic analysis is a crucial step for locating the environmental contamination sources and controlling plastic contamination. A popular tool like Fourier transform infrared (FTIR) spectroscopy is capable of identifying plastic types and can be carried out through a variety of containers. Unfortunately, sample collection from water sources like rivers usually involves filtration so the measurements inevitably include the membrane filter that also has its own FTIR characteristic bands. Furthermore, when plastic particles are small, the membrane filter's spectrum may overwhelm the desired plastics' spectrum. In this study, we proposed a novel preprocessing method based on the dictionary learning technique for decomposing the variations within the acquired FTIR spectra and capturing the membrane filter's characteristic bands for the effective removal of these unwanted signals. We break down the plastic analysis task into two subtasks - membrane filter removal and plastic classification - to increase the explainability of the method. In the experiments, our method demonstrates a 1.5-fold improvement compared with baseline, and yields comparable results compared to other state-of-the-art methods such as UNet when applied to noisy spectra with low signal-to-noise ratio (SNR), but offers explainability, a crucial quality that is missing in other state-of-the-art methods. The limitations of the method are studied by testing against generated spectra with different levels of noise, with SNR ranging from 0 to - 30dB, as well as samples collected from the lab. The components/atoms learned from the dictionary learning technique are also scrutinized to describe the explainability and demonstrate the effectiveness of our proposed method in practical applications.
微塑料分析是定位环境污染源和控制塑料污染的关键步骤。像傅里叶变换红外（FTIR）光谱这样的常用工具能够识别塑料类型，并且可以通过各种容器进行分析。不幸的是，从河流等水源采集样本通常需要过滤，因此测量结果不可避免地会包含同样具有自身FTIR特征谱带的膜过滤器。此外，当塑料颗粒很小时，膜过滤器的光谱可能会掩盖所需塑料的光谱。在本研究中，我们提出了一种基于字典学习技术的新型预处理方法，用于分解采集到的FTIR光谱中的变化，并捕捉膜过滤器的特征谱带，以有效去除这些不需要的信号。我们将塑料分析任务分解为两个子任务——去除膜过滤器和塑料分类——以提高该方法的可解释性。在实验中，与基线相比，我们的方法显示出1.5倍的改进，并且在应用于低信噪比（SNR）的噪声光谱时，与其他诸如UNet等先进方法产生的结果相当，但具有可解释性，这是其他先进方法所缺乏的关键特性。通过针对不同噪声水平（SNR范围从0到 - 30dB）的生成光谱以及从实验室收集的样本进行测试，研究了该方法的局限性。还对从字典学习技术中学到的成分/原子进行了审查，以描述可解释性，并证明我们提出的方法在实际应用中的有效性。
