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Sweiss, M, Assi, S, Barhoumi, L, Al-Jumeily, D, Watson, M, Wilson, M, Arnot, T and Scott, R (2023) Qualitative and quantitative evaluation of microalgal biomass using portable attenuated total reflectance-Fourier transform infrared spectroscopy and machine learning analytics. Journal of Chemical Technology and Biotechnology. ISSN 0268-2575
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Qualitative and quantitative evaluation of microalgal biomass using portable attenuated total reflectance-Fourier transform infrared spectroscopy and machine learning analytics.pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
BACKGROUND: Using 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. RESULTS: The 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. CONCLUSION: ATR-FTIR spectroscopy offered a rapid and sustainable method for monitoring the microalgal biomass produced during wastewater treatment processes.
| Item Type: | Article |
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| Uncontrolled Keywords: | Biotechnology |
| Subjects: | Q Science > Q Science (General) T Technology > TD Environmental technology. Sanitary engineering |
| Divisions: | Computer Science & Mathematics Pharmacy & Biomolecular Sciences |
| Publisher: | Wiley |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 22 Nov 2023 16:56 |
| Last Modified: | 22 Nov 2023 16:56 |
| DOI or ID number: | 10.1002/jctb.7512 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/21924 |
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