Accurate classification of microalgae by intelligent frequency-division-multiplexed fluorescence imaging flow cytometry

Abstract

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.