基于拉曼光谱微塑料数据光谱特征的聚合物类型机器学习
Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data
AI总结：
本文利用 SLOPP 拉曼光谱库训练多种机器学习模型，并经预处理与增强后用随机森林识别环境老化微塑料的22类聚合物，使 SloPP-E 上的分类准确率由89%提升至93.81%。
摘要：
当前用于分析化合物结构、识别微塑料中聚合物类型的工具和技术，尚未针对经历环境风化的微塑料进行良好校准。与未暴露于风化过程的微塑料样品相比，被环境风化因素降解的微塑料在分析上的确定性更低。机器学习工具和技术使我们能够更好地校准研究工具，以提高微塑料分析的确定性。本文研究了在样品未受环境降解影响时，其光谱特征（拉曼位移值）是否具有足够的区分度，使经过充分研究的机器学习（ML）算法能够利用相对少量的标注输入数据学会识别聚合物类型。研究在知名数据库 Spectral Libraries of Plastic Particles（SLOPP）上训练了多种 ML 模型，该库包含一系列塑料颗粒的拉曼位移和强度结果；随后在由22种聚合物类型组成的环境老化塑料颗粒数据集（SloPP-E）上进行测试。经过大量预处理和数据增强后，训练得到的随机森林模型在 SloPP-E 数据集上接受测试，分类准确率从89%提升至93.81%。
英文摘要：
The tools and technology that are currently used to analyze chemical compound structures that identify polymer types in microplastics are not well-calibrated for environmentally weathered microplastics. Microplastics that have been degraded by environmental weathering factors can offer less analytic certainty than samples of microplastics that have not been exposed to weathering processes. Machine learning tools and techniques allow us to better calibrate the research tools for certainty in microplastics analysis. In this paper, we investigate whether the signatures (Raman shift values) are distinct enough such that well studied machine learning (ML) algorithms can learn to identify polymer types using a relatively small amount of labeled input data when the samples have not been impacted by environmental degradation. Several ML models were trained on a well-known repository, Spectral Libraries of Plastic Particles (SLOPP), that contain Raman shift and intensity results for a range of plastic particles, then tested on environmentally aged plastic particles (SloPP-E) consisting of 22 polymer types. After extensive preprocessing and augmentation, the trained random forest model was then tested on the SloPP-E dataset resulting in an improvement in classification accuracy of 93.81% from 89%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
