Accurate Classification Of Microalgae By Intelligent Frequency-Division-Multiplexed Fluorescence Imaging Flow Cytometry

Microalgae have recently been gaining attention for their versatile uses and environmentally friendly benefits. Accurate characterization and classification of a large population of microalgal cells with single-cell resolution are highly valuable for their diverse applications such as water treatment, biofuel production, food, and nitrogen-fixing biofertilization. Here we demonstrate accurate classification of spherical microalgal species using recently developed frequency-division-multiplexed fluorescence imaging flow cytometry and machine learning. We obtained three-color (bright-field and two-color fluorescence) images of microalgal cells, quantified morphological features of the cells using the images, and classified six microalgae using features via a support vector machine. By virtue of the rich information content of the three-color images of microalgal cells, we classified six microalgae with a high accuracy of 99.8%. Our method can evaluate large populations of microalgal cells with single-cell resolution and hence holds promise for various applications such as environmental monitoring of the hydrosphere. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement