Abstract:
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.
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