基于偏振光散射与荧光测量的水中悬浮颗粒现场快速分类仪

Field prototype for rapid classification of suspended particles in water based on polarized light scattering and fluorescence measurement

水中悬浮颗粒是水体物质的重要成分,因此监测它们的种类和浓度对研究和保护水生态系统具有重要科学意义和实用价值。文中研制了一种水中悬浮颗粒分类仪(Suspended Particle Classifier,SPC),旨在现场检测野外采集的水样,快速得出水中悬浮颗粒的种类、数量和比例。SPC采用引流管将颗粒输送至散射体积内,通过同时探测单个颗粒的散射光偏振态和荧光信号,结合机器学习算法对颗粒分类。对沉积物、微塑料和微藻的标准样品做了数据集并训练分类器,SPC能以大于95%的预测正确率对它们进行分类。接着,将SPC和商业水质多参数监测仪(QWA)同时在崖门水道连续布放25个小时。SPC能快速测量现场采集的水样,获取不同水层的沉积物、微塑料和微藻的数量随时间的变化情况。SPC给出的微藻数量与QWA测得的叶绿素a浓度以及藻红蛋白浓度之间存在显著的相关性;此外,SPC给出的沉积物等效时间截面和QWA测得的浊度值也呈现出明显的相关性,由此可以证明SPC分类结果的可靠性。结果表明:SPC能够对水中的悬浮颗粒进行现场快速分类检测,有望成为探索水生态系统的关键技术。

Objective Suspended particles in water include solid or liquid particles,such as sediment,microplastics,and microalgae.Accurate monitoring of their categories and concentration is of great scientific and practical significance for studying and protecting aquatic ecosystems.Various optical instruments have been developed to probe suspended particles in water,which can be divided into two categories based on the measurement methods.One category measures the overall characteristics of all particles in a body of water,while the other measures individual particles.Water Quality Analyzer(QWA)provide estimates of particle concentration and size distribution,chlorophyll-a concentration,and other water quality parameters.However,QWA are limited in their ability to accurately identify the categories of suspended particles in water.Underwater flow cytometry enables the characterization of various categories of particles by breaking up a water sample into individual particles that are then to be measured.However,this technique is expensive and requires complex sample pretreatment,which limits its application.Therefore,it is needed to develop a prototype for field detection of water samples collected in the wild,with the goal of quickly determining the categories,numbers,and proportions of suspended particles in water.Methods Suspended Particle Classifier(SPC)has been developed in this paper and its diagram is depicted(Fig.1).The SPC employs a 445 nm laser as the excitation source to induce chlorophyll fluorescence,and the polarization state of the laser is modulated by a polarization state generator.The SPC obtains individual particle polarized light scattering and fluorescence signals,which are combined with a Support Vector Machine(SVM)to classify particles based on their optical properties.To ensure its suitability for field use,the SPC is equipped with a drainage tube for the transportation of water samples and an industrial computer for instrument control and data analysis.Standard samples of sediments,microplastics,and microalgae are collected.Then,datasets are created to train the SVM classifier.Subsequently,SPC was deployed alongside QWA in the Yamen Waterway for 25 hours to evaluate its performance(Fig.3).The accuracy of the SPC classification was verified using data obtained from the QWA.Results and Discussions The SPC's classification accuracy for standard samples of sediment,microplastics,and microalgae was found to be 95.3%,93.3%,and 97.9%(Fig.4),respectively,indicating that the classifier has good performance in classifying these particles.The average accuracy and recall rate were found to be 95.5%(Tab.1),indicating the SVM model has strong feature extraction ability.These results suggest that the SPC can accurately classify standard samples.When applied in the Yamen Waterway,the SPC was able to rapidly measure water samples collected in the field and track the changes in the number of sediments,microplastic,and microalgae in different water layers over time(Fig.5).Furthermore,the number of microalgae identified by the SPC was found to have a strong correlation with the concentration of chlorophyll-a and phycoerythrin measured by the QWA(Fig.6,Tab.2).Additionally,the so-called effective time cross-section of sediments identified by the SPC was found to have a strong correlation with the turbidity value measured by the QWA(Fig.6,Tab.2),further validating the reliability of the SPC's classification results.Conclusions In this study,a suspended particle classifier was developed with the aim of classifying and counting suspended particles in water samples collected in the field.The SPC probes polarized light scattering and fluorescence signals from individual suspended particles and uses SVM to classify them based on their optical properties.The classification accuracy for standard samples of sediment,microplastics,and microalgae was over 95%.To validate the SPC's classification ability for field water samples,the SPC and QWA were deployed in the Yamen Waterway for 25 hours of synchronous testing.The SPC was able to track changes in the number of sediment,microplastic,and microalgae in different water layers over time.There was a strong correlation between the SPC and QWA measurement data,indicating the high reliability of the SPC in classifying particles in field water samples.These results demonstrate that the SPC can rapidly detect and classify suspended particles in water and has the potential to be a valuable tool for exploring aquatic ecosystems.