Lei Benjamin, Bissonnette Justine R, Hogan Úna E, Bec Avery E, Feng Xinyi, Smith Rodney D L
Department of Chemistry, University of Waterloo, 200 University Avenue W., Waterloo, OntarioN2L 3G1, Canada.
Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Avenue W., Waterloo, OntarioN2L 3G1, Canada.
Anal Chem. 2022 Dec 13;94(49):17011-17019. doi: 10.1021/acs.analchem.2c02451. Epub 2022 Nov 29.
Raman spectroscopy is commonly used in microplastics identification, but equipment variations yield inconsistent data structures that disrupt the development of communal analytical tools. We report a strategy to overcome the issue using a database of high-resolution, full-window Raman spectra. This approach enables customizable analytical tools to be easily created─a feature we demonstrate by creating machine-learning classification models using open-source random-forest, K-nearest neighbors, and multi-layer perceptron algorithms. These models yield >95% classification accuracy when trained on spectroscopic data with spectroscopic data downgraded to 1, 2, 4, or 8 cm spacings in Raman shift. The accuracy can be maintained even in non-ideal conditions, such as with spectroscopic sampling rates of 1 kHz and when microplastic particles are outside the focal plane of the laser. This approach enables the creation of classification models that are robust and adaptable to varied spectrometer setups and experimental needs.
拉曼光谱常用于微塑料识别，但设备差异会产生不一致的数据结构，从而干扰通用分析工具的开发。我们报告了一种使用高分辨率全窗口拉曼光谱数据库来克服这一问题的策略。这种方法能够轻松创建可定制的分析工具，我们通过使用开源随机森林、K近邻和多层感知器算法创建机器学习分类模型来展示这一特性。当在拉曼位移中光谱数据被降级为1、2、4或8厘米间距的光谱数据上进行训练时，这些模型的分类准确率超过95%。即使在非理想条件下，如光谱采样率为1千赫兹以及微塑料颗粒不在激光焦平面内时，准确率也能得以维持。这种方法能够创建出强大且能适应各种光谱仪设置和实验需求的分类模型。
