Qualitative and quantitative evaluation of microalgal biomass using portable attenuated total reflectance‐Fourier transform infrared spectroscopy and machine learning analytics

Author:

Sweiss Mais1ORCID,Assi Sulaf2ORCID,Barhoumi Lina3,Al‐Jumeily Dhiya4,Watson Megan2,Wilson Megan2,Arnot Tom5ORCID,Scott Rod6

Affiliation:

1. Department of Biotechnology, Faculty of Agricultural Technology Al‐Balqa Applied University Al‐Salt Jordan

2. School Pharmacy and Biomolecular Sciences Liverpool John Moores University Liverpool UK

3. Department of Chemistry, Faculty of Science Al‐Balqa Applied University Al‐Salt Jordan

4. School of Computer Science and Mathematics Liverpool John Moores University Liverpool UK

5. Water Research & Innovation Centre, Department of Chemical Engineering University of Bath Bath UK

6. Department of Biology and Biochemistry University of Bath Bath UK

Abstract

AbstractBACKGROUNDUsing microalgae for wastewater treatment offers an environmentally friendly method to produce microalgal biomass that can be used for many applications. However, the biochemical characteristics of microalgal biomass vary from species to species, from strain to strain, and between different growth stages within the same species/strain. This study utilized portable attenuated total reflectance‐Fourier transform infrared (ATR‐FTIR) spectroscopy to determine the composition of freeze‐dried microalgal biomass corresponding to eight different locally isolated microalgae and a reference strain that were grown in wastewater and then harvested at the log and stationary phases, respectively.RESULTSThe results showed that the portable ATR‐FTIR spectroscopy offered a rapid, non‐destructive, and accurate technique for monitoring changes in the biochemical composition of algal biomass at stationary and log phases, as well as quantifying their main constituents. For qualitative analysis of species, two machine learning analytics (MLAs; correlation in wavenumber space and principal component analysis) were able to differentiate between microalgae isolates in both their stationary and log phases. For quantification, univariate or multivariate regression offered accuracy in quantifying key microalgal constituents related to proteins, lipids, and carbohydrates. In this sense, multivariate methods showed more accuracy for quantifying carbohydrates, yet proteins and lipids were more accurately quantified with univariate regression. Based on quantification, the highest relative content of carbohydrates in the log phase was for Jordan‐23 (Jo‐23; Desmodesmus sp.), whereas the highest content in the stationary phase was that for Jordan‐29 (Jo‐29; Desmodesmus sp). Regarding the relative lipid content in the log phase, Jo‐23 had the highest lipid content, while the highest content in the stationary phase was for Jo‐29.CONCLUSIONATR‐FTIR spectroscopy offered a rapid and sustainable method for monitoring the microalgal biomass produced during wastewater treatment processes. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).

Publisher

Wiley

Subject

Inorganic Chemistry,Organic Chemistry,Pollution,Waste Management and Disposal,Fuel Technology,Renewable Energy, Sustainability and the Environment,General Chemical Engineering,Biotechnology

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