Grand Clément, Scotté Camille, Prado Énora, El Rakwe Maria, Fauvarque Olivier, Rigneault Hervé
Aix Marseille Univ, CNRS, Centrale Med, Institut Fresnel, Marseille, France.
INRAE, UMR ITAP, 361 Rue Jean François Breton, Montpellier 34090, France.
Environ Technol Innov. 2024 May;34:103622. doi: 10.1016/j.eti.2024.103622.
The fast and reliable detection of micron-sized plastic particles from the natural marine environment is an important topic that is mostly addressed using spontaneous Raman spectroscopy. Due to the long (>tens of ms) integration time required to record a viable Raman signal, measurements are limited to a single point per microplastic particle or require very long acquisition times (up to tens of hours). In this work, we develop, validate, and demonstrate a compressive Raman technology using binary spectral filters and single-pixel detection that can image and classify six types of marine microplastic particles over an area of 1 mm with a pixel dwell time down to 1.75 ms/pixel and a spatial resolution of 1 µm. This is x10-100 faster than reported in previous studies.
从天然海洋环境中快速可靠地检测微米级塑料颗粒是一个重要课题，目前大多通过自发拉曼光谱法来解决。由于记录可用拉曼信号需要较长（>数十毫秒）的积分时间，测量仅限于每个微塑料颗粒的单个点，或者需要很长的采集时间（长达数十小时）。在这项工作中，我们开发、验证并展示了一种使用二元光谱滤波器和单像素检测的压缩拉曼技术，该技术能够在1平方毫米的区域内对六种海洋微塑料颗粒进行成像和分类，像素驻留时间低至1.75毫秒/像素，空间分辨率为1微米。这比之前研究报告的速度快10至100倍。
