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Harmful algal blooms (HABs) pose a global threat to the biodiversity and stability of local aquatic ecosystems. Rapid and accurate classification of microalgae and cyanobacteria in water is increasingly desired for monitoring complex water environments. In this paper, we propose a pulse feature-enhanced classification (PFEC) method as a potential solution. Equipped with a rapid measurement prototype that simultaneously detects polarized light scattering and fluorescence signals of individual particles, PFEC allows for the extraction of 38 pulse features to improve the classification accuracy of microalgae, cyanobacteria, and other suspended particulate matter (SPM) to 89.03%. Compared with microscopic observation, PFEC reveals three phyla proportions in aquaculture samples with an average error of less than 14%. In this paper, PFEC is found to be more accurate than the pulse-average classification method, which is interpreted as pulse features carrying more detailed information about particles. The high consistency of the dominant and common species between PFEC and microscopy in all field samples also demonstrates the flexibility and robustness of the former. Moreover, the high Pearson correlation coefficient accounting for 0.958 between the cyanobacterial proportion obtained by PFEC and the cyanobacterial density given by microscopy implies that PFEC serves as a promising early warning tool for cyanobacterial blooms. The results of this work suggest that PFEC holds great potential for the rapid and accurate classification of microalgae and cyanobacteria in aquatic environment monitoring.

Microalgae play a vital role in aquatic ecological research, but the fine classification of these tiny and various microalgae cells is still challenging for the community. In this paper, we propose a multimodality technique to simultaneously acquire the polarized light scattering, fluorescence and laser-induced acoustic wave signals originated from individual microalgae cells in water. Experiments of different species of Spirulina and different states of Microcystis have been conducted to test our experiment setup, and the results demonstrate that this method can well discriminate microalgae cells with pigment or microstructural differences. Moreover, with these modalities, the consumption of absorbed energy is evaluated quantitively, and a possible way to assess photosynthesis on a single-cell level is presented. This work is expected to be a powerful technique to probe the biophysical states of microalgae in the aquatic ecosystem.

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.

Physiological states of marine microalgal cells can influence photosynthesis efficiency, which affects approximately half of global carbon fixation. The detection of the algae physiological profiles is important for marine ecology and economy. In this paper, we propose a polarized light-scattering method to detect sensitive changes in the physiological states of the suspended marine microalgal cells. Our experimental setup is designed to measure the scattered polarization parameters of the cells suspended individually in the seawater. Two species of microalgal cells cultured in the laboratory were measured for several days. Experimental results showed that both species display distinctive changes in their polarized photon scattering features corresponding to changes in their physiological states. The changes are far more prominent than those displayed in unpolarized light scattering. Microscopy observations, simulations for microspheres of different diameters and refractive indices, or different shapes, indicated that the polarization features of the scattered photons are sensitive to the submicrometer microstructures of the cells. This study demonstrates the potential of the polarized light-scattering technique to characterize the physiological states of suspended marine microalgae.

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. [ABSTRACT FROM AUTHOR]

Nanoplastics have been found in marine biological habitats. There is now an urgent need to effectively detect these nanoplastics. Due to the small size of these plastics, it is challenging to directly measure the total abundance while in a suspension. In this study, a backward 120° polarized light scattering device was used to measure the polarization parameters of Phaeodactylum tricornutum , before and after the adsorption of nanoplastics. The polarization parameters were observed to be affected by the adsorption of nanoplastics, with different concentrations and sizes. Moreover, the polarization parameter X changed with time after mixing the nanoplastics with the microalgae, which showed the details of the adsorption of nanoplastics by the microalgae, and was further confirmed by a scanning electron microscope image. The simulation results demonstrated how the polarization parameter X of the scatterers (e.g. microalgae) were affected by the adsorption of the nanoplastics. The results of this work indicate that the polarization scattering method can be used to probe the concentration and size of nanoplastics adsorbed by microalgae in water. [ABSTRACT FROM AUTHOR]

• Microalgae can be well classified by the polarization fingerprint with explicit physical meanings. • Polarization fingerprint has extensibility and flexibility in practice. • Polarization fingerprint is potential to help retrieve in-situ information of microalgae. Microalgae classification is important for aquatic environment monitoring, but it is difficult in some in-situ cases. The Mueller matrix is known to encode the physical properties including the sophisticated microstructure of microalgae cells. In this paper, the concept of polarization fingerprint based on the Mueller matrix is presented to extract the parameters to classify microalgae. An experimental setup based on the particulate Mueller matrix polarimetry (PMP) is used to measure 25 kinds of microalgae covering seven phyla to form a Mueller matrix dataset. The proposed sixteen polarization parameters with explicit physical meanings and associations with the structural properties are selected to compose the polarization fingerprint. As a result, different microalgae can be effectively classified by the polarization fingerprint and machine learning algorithms. The contribution of the parameters in the polarization fingerprint is analyzed to provide the specific features for microalgae classification, which gives insights into microalgae's exclusive physical properties. The extensibility and flexibility of the polarization fingerprint are discussed. This work shows the polarization fingerprint's power to classify microalgae, which can help retrieve in-situ information on the community structures of microalgae, and further monitor the aquatic environment. [ABSTRACT FROM AUTHOR]