Qualitative and quantitative evaluation of microalgal biomass using portable attenuated total reflectance‐Fourier transform infrared spectroscopy and machine learning analytics
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 differe...
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| Veröffentlicht in: | Journal of chemical technology and biotechnology (1986) 2024-01, Vol.99 (1), p.92-108 |
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| container_title | Journal of chemical technology and biotechnology (1986) |
| container_volume | 99 |
| creator | Sweiss, Mais Assi, Sulaf Barhoumi, Lina Al‐Jumeily, Dhiya Watson, Megan Wilson, Megan Arnot, Tom Scott, Rod |
| description | 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. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI). |
| doi_str_mv | 10.1002/jctb.7512 |
| format | Article |
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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. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).</description><identifier>ISSN: 0268-2575</identifier><identifier>EISSN: 1097-4660</identifier><identifier>DOI: 10.1002/jctb.7512</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Algae ; Aquatic microorganisms ; attenuated total reflectance Fourier transform infrared ; Biochemical characteristics ; Biochemical composition ; Biochemistry ; Biomass ; Biotechnology ; Carbohydrates ; Chemical technology ; Composition ; Constituents ; Desmodesmus ; Fourier transforms ; Infrared analysis ; Infrared spectroscopy ; Learning algorithms ; Learning analytics ; Lipids ; Machine learning ; Microalgae ; Monitoring ; Monitoring methods ; Multivariate analysis ; Phases ; Portability ; principal component analysis ; Principal components analysis ; Proteins ; Qualitative analysis ; Reflectance ; spectroscopy ; Spectrum analysis ; Stationary phase ; wastewater ; Wastewater treatment ; Water treatment ; Wavelengths</subject><ispartof>Journal of chemical technology and biotechnology (1986), 2024-01, Vol.99 (1), p.92-108</ispartof><rights>2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).</rights><rights>2023. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3322-1afc44f8f9acb758c71553ce23ff491e9ce627ab0eb795556f79dbf64f7760333</citedby><cites>FETCH-LOGICAL-c3322-1afc44f8f9acb758c71553ce23ff491e9ce627ab0eb795556f79dbf64f7760333</cites><orcidid>0000-0002-7950-9429 ; 0000-0002-1512-9224 ; 0000-0002-5142-9179</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjctb.7512$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjctb.7512$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Sweiss, Mais</creatorcontrib><creatorcontrib>Assi, Sulaf</creatorcontrib><creatorcontrib>Barhoumi, Lina</creatorcontrib><creatorcontrib>Al‐Jumeily, Dhiya</creatorcontrib><creatorcontrib>Watson, Megan</creatorcontrib><creatorcontrib>Wilson, Megan</creatorcontrib><creatorcontrib>Arnot, Tom</creatorcontrib><creatorcontrib>Scott, Rod</creatorcontrib><title>Qualitative and quantitative evaluation of microalgal biomass using portable attenuated total reflectance‐Fourier transform infrared spectroscopy and machine learning analytics</title><title>Journal of chemical technology and biotechnology (1986)</title><description>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. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).</description><subject>Algae</subject><subject>Aquatic microorganisms</subject><subject>attenuated total reflectance Fourier transform infrared</subject><subject>Biochemical characteristics</subject><subject>Biochemical composition</subject><subject>Biochemistry</subject><subject>Biomass</subject><subject>Biotechnology</subject><subject>Carbohydrates</subject><subject>Chemical technology</subject><subject>Composition</subject><subject>Constituents</subject><subject>Desmodesmus</subject><subject>Fourier transforms</subject><subject>Infrared analysis</subject><subject>Infrared spectroscopy</subject><subject>Learning algorithms</subject><subject>Learning analytics</subject><subject>Lipids</subject><subject>Machine learning</subject><subject>Microalgae</subject><subject>Monitoring</subject><subject>Monitoring methods</subject><subject>Multivariate analysis</subject><subject>Phases</subject><subject>Portability</subject><subject>principal component analysis</subject><subject>Principal components analysis</subject><subject>Proteins</subject><subject>Qualitative analysis</subject><subject>Reflectance</subject><subject>spectroscopy</subject><subject>Spectrum analysis</subject><subject>Stationary phase</subject><subject>wastewater</subject><subject>Wastewater treatment</subject><subject>Water treatment</subject><subject>Wavelengths</subject><issn>0268-2575</issn><issn>1097-4660</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp1kU1uFDEQhS0EEkPIIjewxIpFJ7a7bU8vYUR-UCSEFNatak85eOS2O7Y7aHYcgbNwJE4STwaWrMoqfe_52Y-QM87OOWPiYmfKeK4lFy_IirNeN51S7CVZMaHWjZBaviZvct4xxtRaqBX5_XUB7woU94gUwpY-LBDKvwU-gl_qMQYaLZ2cSRH8PXg6ujhBznTJLtzTOaYCo68GpWCoAtzSEkvlElqPpkAw-Ofnr8u4JIeJlgQh25gm6oJNkCqe54qlmE2c9885JjDfXUDqEVI4XAIB_L44k9-SVxZ8xtO_84R8u_x0t7lubr9c3Ww-3DambYVoOFjTdXZtezCjlmujuZStQdFa2_Uce4NKaBgZjrqXUiqr--1oVWe1Vqxt2xPy7ug7p_iwYC7DruavKfIgesZbWQ15pd4fqfo3Odf3DnNyE6T9wNlwqGQ4VDIcKqnsxZH94Tzu_w8Onzd3H58VT_g5lWM</recordid><startdate>202401</startdate><enddate>202401</enddate><creator>Sweiss, Mais</creator><creator>Assi, Sulaf</creator><creator>Barhoumi, Lina</creator><creator>Al‐Jumeily, Dhiya</creator><creator>Watson, Megan</creator><creator>Wilson, Megan</creator><creator>Arnot, Tom</creator><creator>Scott, Rod</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0002-7950-9429</orcidid><orcidid>https://orcid.org/0000-0002-1512-9224</orcidid><orcidid>https://orcid.org/0000-0002-5142-9179</orcidid></search><sort><creationdate>202401</creationdate><title>Qualitative and quantitative evaluation of microalgal biomass using portable attenuated total reflectance‐Fourier transform infrared spectroscopy and machine learning analytics</title><author>Sweiss, Mais ; 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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. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/jctb.7512</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-7950-9429</orcidid><orcidid>https://orcid.org/0000-0002-1512-9224</orcidid><orcidid>https://orcid.org/0000-0002-5142-9179</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algae Aquatic microorganisms attenuated total reflectance Fourier transform infrared Biochemical characteristics Biochemical composition Biochemistry Biomass Biotechnology Carbohydrates Chemical technology Composition Constituents Desmodesmus Fourier transforms Infrared analysis Infrared spectroscopy Learning algorithms Learning analytics Lipids Machine learning Microalgae Monitoring Monitoring methods Multivariate analysis Phases Portability principal component analysis Principal components analysis Proteins Qualitative analysis Reflectance spectroscopy Spectrum analysis Stationary phase wastewater Wastewater treatment Water treatment Wavelengths |
| title | Qualitative and quantitative evaluation of microalgal biomass using portable attenuated total reflectance‐Fourier transform infrared spectroscopy and machine learning analytics |
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