Staining to machine learning: An emerging technology for determination of microalgal cell viability

Abstract

Microalgae are unicellular photosynthetic microorganisms typically found in aquatic environments and they play a vital role in the global carbon and energy cycles. Discrimination of dead and live cells is an important factor in microalgae research and environmental monitoring. Numerous research on the effects of various microalgae has been conducted concerning cell viability. Recently, dyes such as Trypan Blue (TB), Evans Blue (EB), and Neutral Red (NR) have been employed to assess the viability of microalgae. Existing approaches for identifying dead and living microalgal cells all have flaws, such as the requirement for staining and pre-treatment. A machine learning method was created to distinguish the living and dead microalgal cells by using of a digital holography microscopy, and the accuracy of this technique was greater. The machine learning method offers a new way of studying both freshwater and marine microalgal cell cultures. This review focuses on the existing methods and emerging technology for determining dead and living microalgae cells. This review work will enlighten the new research for the detection of live or dead microalgae.