Simultaneously Acquiring Optical and Acoustic Properties of Individual Microalgae Cells Suspended in Water

Multiwavelength Photoacoustic Doppler Flowmetry of Living Microalgae Cells

Photoacoustics can provide a direct measurement of light absorption by microalgae depending on the photosynthesis pigment within them. In this study, we have performed photoacoustic flowmetry on living microalgae cells to measure their flow characteristics, which include flow speed, flow angle, flow direction, and, more importantly, the photoacoustic absorption spectrum, all by observing the photoacoustic Doppler power spectra during their flowing state. A supercontinuum pulsed laser with a high repetition frequency is used as the light source: through intensity modulation at a specified frequency, it can provide wavelength-selectable excitation of a photoacoustic signal centered around this frequency. Our approach can be useful to simultaneously measure the flow characteristics of microalgae and easily discriminate their different species with high accuracy in both static and dynamic states, thus facilitating the study of their cultivation and their role in our ecosystem.

Rapidly Measuring Scattered Polarization Parameters of the Individual Suspended Particle with Continuously Large Angular Range

Suspended particles play a vital role in aquatic environments. We propose a method to rapidly measure the scattered polarization parameters of individual suspended particles with continuously large angular range (PCLAR), from 60° to 120° in one shot. A conceptual setup is built to measure PCLAR with 20 kHz; to verify the setup, 10 μm-diameter silica microspheres suspended in water, whose PCLAR are consistent with those simulated by Mie theory, are measured. PCLAR of 6 categories of particles are measured, which enables high-accuracy classification with the help of a convolutional neural network algorithm. PCLAR of different mixtures of Cyclotella stelligera and silica microspheres are measured to successfully identify particulate components. Furthermore, classification ability comparisons of different angular-selection strategies show that PCLAR enables the best classification beyond the single angle, discrete angles and small-ranged angles. Simulated PCLAR of particles with different size, refractive index, and structure show explicit discriminations between them. Inversely, the measured PCLAR are able to estimate the effective size and refractive index of individual Cyclotella cells. Results demonstrate the method’s power, which intrinsically takes the advantage of the optical polarization and the angular coverage. Future prototypes based on this concept would be a promising biosensor for particles in environmental monitoring.

Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data

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.