Microalgae biomass and biomolecule quantification: Optical techniques, challenges and prospects

Microalgae have emerged as a potential raw material to produce a spectrum of industrially significant biomolecules such as lipids, proteins, and carotenoids. Nevertheless, the progress on industrial-scale microalgae cultivation and biomolecules harvesting is unsatisfactory. There still exists several major technical challenges associated with the optimization, monitoring, and extraction of biomolecules in microalgae. This review focuses on the state-of-the-art methods to monitor microalgae biomass and biomolecules. Some of the common methods used to monitor the biomass and biomolecules in the microalgae culture include manual cell counting, spectroscopy measurements, and colorimetric methods. Key drawbacks of these methods such as processing time, accuracy, reliability, and compatibility with real time data acquisition are discussed to explore new approaches for future development. Apart from a systematic evaluation of the importance of these microalgae biomolecules to various industries, this review also provides comparative analysis to highlight the important aspects of microalgae monitoring methods such as accuracy, optical detection wavelength range, and the characteristics of these measurement techniques. Potential approaches to improve the efficiency of optical methods in monitoring microalgae culture are also emphasized. This review also elucidates the potential of machine learning and big data analytics algorithms in understanding the growth and interaction of microalgae strains as well as to aid in decision making, which can possibly encourage the participation of small-scale and large-scale farmers in microalgae cultivation. Information corroborated in this review is expected to provide an important roadmap for future research in monitoring biomass and quantifying biomolecules in microalgae by optical techniques.

Automated summary

Microalgae have the potential to produce industrially significant biomolecules, but there are still technical challenges in industrial-scale cultivation and biomolecules harvesting. This review focuses on monitoring methods for microalgae biomass and biomolecules, discussing drawbacks of current methods and proposing potential approaches for improvement. The review also highlights the potential of machine learning and big data analytics in understanding microalgae growth and aiding decision making.