An aberration-free line scanning confocal Raman imager (named AFLSCRI) is developed to achieve rapid Raman imaging. As an application example, various types and sizes of MPs are identified through Raman imaging combined with a machine learning algorithm. The system has excellent performance with a spatial resolution of 2 µm and spectral resolution of 4 cm. Compared to traditional point-scanning Raman imaging systems, the detection speed is improved by 2 orders of magnitude. The pervasive nature of MPs results in their infiltration into the food chain, raising concerns for human health due to the potential for chemical leaching and the introduction of persistent organic pollutants. We conducted a series of experiments on various types and sizes of MPs. The system can give a classification accuracy of 98% for seven different types of plastics, and Raman imaging and species identification for MPs as small as 1 µm in diameter were achieved. We also identified toxic and harmful substances remaining in plastics, such as Dioctyl Phthalate (DOP) residues. This demonstrates a strong performance in microplastic species identification, size recognition and identification of hazardous substance contamination in microplastics.

中文翻译：

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无像差线扫描共焦拉曼成像仪及微塑料的类型分类和分布检测

开发了无像差线扫描共焦拉曼成像仪（AFLSCRI）以实现快速拉曼成像。作为一个应用示例，通过拉曼成像结合机器学习算法来识别各种类型和尺寸的MP。该系统具有优异的性能，空间分辨率为2 µm，光谱分辨率为4 cm。与传统的点扫描拉曼成像系统相比，检测速度提高了2个数量级。 MP 的普遍性导致它们渗透到食物链中，由于化学浸出的可能性和持久性有机污染物的引入，引起了对人类健康的担忧。我们对各种类型和大小的 MP 进行了一系列实验。该系统可以对七种不同类型的塑料进行98%的分类准确率，并实现了直径小至1 µm的MP的拉曼成像和物种鉴定。我们还鉴定出塑料中残留的有毒有害物质，例如邻苯二甲酸二辛酯（DOP）残留物。这表明在微塑料物种识别、尺寸识别以及微塑料中有害物质污染识别方面具有强大的性能。