Microplastic research is a rapidly developing field, with urgent needs for high throughput and automated analysis techniques. We conducted a review covering image analysis from optical microscopy, scanning electron microscopy, fluorescence microscopy, and spectral analysis from Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, pyrolysis gas–chromatography mass–spectrometry, and energy dispersive X-ray spectroscopy. These techniques were commonly used to collect, process, and interpret data from microplastic samples. This review outlined and critiques current approaches for analysis steps in image processing (color, thresholding, particle quantification), spectral processing (background and baseline subtraction, smoothing and noise reduction, data transformation), image classification (reference libraries, morphology, color, and fluorescence intensity), and spectral classification (reference libraries, matching procedures, and best practices for developing in-house reference tools). We highlighted opportunities to advance microplastic data analysis and interpretation by (i) quantifying colors, shapes, sizes, and surface topologies with image analysis software, (ii) identifying threshold values of particle characteristics in images that distinguish plastic particles from other particles, (iii) advancing spectral processing and classification routines, (iv) creating and sharing robust spectral libraries, (v) conducting double blind and negative controls, (vi) sharing raw data and analysis code, and (vii) leveraging readily available data to develop machine learning classification models. We identified analytical needs that we could fill and developed supplementary information for a reference library of plastic images and spectra, a tutorial for basic image analysis, and a code to download images from peer reviewed literature. Our major findings were that research on microplastics was progressing toward the use of multiple analytical methods and increasingly incorporating chemical classification. We suggest that new and repurposed methods need to be developed for high throughput screening using a diversity of approaches and highlight machine learning as one potential avenue toward this capability.

中文翻译：

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EXPRESS：微塑料研究中图像和光谱处理和分类技术的批判性审查

微塑料研究是一个快速发展的领域，迫切需要高通量和自动化分析技术。我们进行了一项综述，涵盖光学显微镜、扫描电子显微镜、荧光显微镜的图像分析以及傅里叶变换红外 (FT-IR) 光谱、拉曼光谱、热解气相色谱质谱和能量色散 X 射线光谱的光谱分析. 这些技术通常用于收集、处理和解释来自微塑料样品的数据。这篇综述概述并批评了图像处理（颜色、阈值、粒子量化）、光谱处理（背景和基线减法、平滑和降噪、数据变换）、图像分类（参考库、形态学、颜色、和荧光强度）和光谱分类（参考库、匹配程序和开发内部参考工具的最佳实践）。我们强调了通过 (i) 使用图像分析软件量化颜色、形状、尺寸和表面拓扑结构，(ii) 识别图像中将塑料颗粒与其他颗粒区分开来的颗粒特征阈值，来推进微塑料数据分析和解释的机会，(iii) ) 推进光谱处理和分类程序，(iv) 创建和共享强大的光谱库，(v) 进行双盲和阴性对照，(vi) 共享原始数据和分析代码，以及 (vii) 利用现成的数据来开发机器学习分类模型。我们确定了可以满足的分析需求，并为塑料图像和光谱参考库、基本图像分析教程以及从同行评审文献中下载图像的代码开发了补充信息。我们的主要发现是，对微塑料的研究正朝着使用多种分析方法的方向发展，并越来越多地纳入化学分类。我们建议需要开发新的和重新利用的方法来使用多种方法进行高通量筛选，并强调机器学习是实现此功能的一个潜在途径。我们的主要发现是，对微塑料的研究正朝着使用多种分析方法的方向发展，并越来越多地纳入化学分类。我们建议需要开发新的和重新利用的方法来使用多种方法进行高通量筛选，并强调机器学习是实现此功能的一个潜在途径。我们的主要发现是，对微塑料的研究正朝着使用多种分析方法的方向发展，并越来越多地纳入化学分类。我们建议需要开发新的和重新利用的方法来使用多种方法进行高通量筛选，并强调机器学习是实现此功能的一个潜在途径。