Probing suspended particles in seawater, such as microalgae, microplastics and silts, is very important for environmental monitoring and ecological research. We propose a method based on polarized light scattering to differentiate different suspended particles massively and rapidly. The optical path follows a similar design of a commonly used marine instrument, BB9, which records backscattering of non-polarized light at 120°. In addition, polarization elements are added to the incident and scattering path for taking polarization measurements. Experiments with polystyrene microspheres, porous polystyrene microspheres, silicon dioxide microspheres, and different marine microalgae show that by carefully choosing the incident polarization state and analyzing the polarization features of the scattered light at 120°, these particles can be effectively differentiated. Simulations based on the Mie scattering theory and discrete dipole approximation (DDA) have also been conducted for particles of different sizes, shapes and refractive indices, which help to understand the relationship between the polarization features and the physical properties of the particles. The laboratory system may serve as a prove-of-concept prototype of new instrumentations for applications on board or even with submersibles.

Microplastics (MPs) have become the widespread contaminants, which raises concerns on their ecological hazards. In-situ detection of MP in water bodies is essential for clear assessment of the ecological risks of MPs. The present study proposes a method based on polarized light scattering which measures the polarization parameters of the scattered light at 120° to detect MP in water. This method takes the advantage of in-situ measurement of the individual particles and the experimental setup in principle is used. By use of the measured polarization parameters equipped by machine learning, the standard polystyrene (PS) spheres, natural water sample, and lab-cultured microalgae are explicitly discriminated, and MP with different physical and chemical properties can be differentiated. It can also characterize the weathering of different MP and identify the specific type from multiple types of MP. This study explores the capability of the proposed method to detect the physical and chemical properties, weathering state and concentration of MP in water which promises the future application in water quality sensing and monitoring.

Microalgae are widely distributed in the ocean, which greatly affects the ocean environment. In this work, a dataset is presented, including the polarized light scattering data of 35 categories of marine microalgae. To analyze the dataset, several machine learning algorithms are applied and compared, such as linear discrimination analysis (LDA) and two types of support vector machine (SVM). Results show that non-linear SVM performs the best among these algorithms. Then, two data preparation approaches for non-linear SVM are compared. Subsequently, more than 10 categories of microalgae out of the dataset can be identified with an accuracy greater than 0.80. The basis of the dataset is shown by finding the categories independent to each other. The discussions about the performance of different incident polarization of light gives some clues to design the optimal incident polarization of light for future instrumentation. With this proposed technique and the dataset, these microalgae can be well differentiated by polarized light scattering data.

In this paper, we used a convolutional neural network to study the classification of marine microalgae by using low-resolution Mueller matrix images. Mueller matrix images of 12 species of algae from 5 families were measured by a Mueller matrix microscopy with an LED light source at 514 nm wavelength. The data sets of seven resolution levels were generated by the bicubic interpolation algorithm. We conducted two groups of classification experiments; one group classified the algae into 12 classes according to species category, and the other group classified the algae into 5 classes according to family category. In each group of classification experiments, we compared the classification results of the Mueller matrix images with those of the first element (M11) images. The classification accuracy of Mueller matrix images declines gently with the decrease of image resolution, while the accuracy of M11 images declines sharply. The classification accuracy of Mueller matrix images is higher than that of M11 images at each resolution level. At the lowest resolution level, the accuracy of 12-class classification and 5-class classification of full Mueller matrix images is 29.89% and 35.83% higher than those of M11 images, respectively. In addition, we also found that the polarization information of different species had different contributions to the classification. These results show that the polarization information can greatly improve the classification accuracy of low-resolution microalgal images.

The vertical migration trend of cyanobacterial cells with gas vesicles in water ecosystems can reflect the changes in the natural environment, such as temperature, nutrients, light conditions, etc. The static pressure treatment is one of the most important approaches to study the properties of the cyanobacterial cell and its gas vesicles. In this paper, a polarized light scattering method is used to probe the collapse and regeneration of the cyanobacterial gas vesicles exposed to different static pressures. During the course, both the axenic and wild type strain of cyanobacterial Microcystis were first treated with different static pressures and then recovered on the normal light conditions. Combining the observation of transmission electron microscopy and floating-sinking photos, the results showed that the collapse and regeneration of the cyanobacterial gas vesicles exposed to different static pressures can be characterized by the polarization parameters. The turbidity as a traditional indicator of gas vesicles but subjected to the concentration of the sample was also measured and found to be correlated with the polarization parameters. More analysis indicated that the polarization parameters are more sensitive and characteristic. The polarized light scattering method can be used to probe the cyanobacterial gas vesicles exposed to different static pressures, which has the potential to provide an in situ rapid and damage-free monitoring tool for observing the vertical migration of cyanobacterial cells and forecasting cyanobacterial blooms.