Extraction of chlorophylls and carotenoids from dry and wet biomass of isolated Chlorella Thermophila: Optimization of process parameters and modelling by artificial neural network.
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| Title: | Extraction of chlorophylls and carotenoids from dry and wet biomass of isolated Chlorella Thermophila: Optimization of process parameters and modelling by artificial neural network. |
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| Authors: | Sarkar, Sambit1 (AUTHOR), Manna, Mriganka Sekhar1 (AUTHOR), Bhowmick, Tridib Kumar2 (AUTHOR), Gayen, Kalyan1 (AUTHOR) kalyan.chemical@nita.ac.in |
| Source: | Process Biochemistry. Sep2020, Vol. 96, p58-72. 15p. |
| Subjects: | Artificial neural networks, Carotenoids, Process optimization, Chlorella, Chlorophyll, Manufacturing processes, Biomass |
| Abstract: | • Extraction of chlorophylls and carotenoids from microalgae using ethanol • Chlorophyll yield from wet biomass was 2.7 fold higher than from dry biomass • Extraction efficiency of ∼90 % was achieved by high speed homogenisation • Boiling and microwave in combination with homogenisation were inefficient • Modelling of the extraction process using Artificial Neural Network (ANN) Chlorophylls and carotenoids can be extracted from microalgae using various solvents. However, there is lack of studies regarding the comparison of extraction yield of these pigments from wet and dry microalgal biomass using different combination of cell disruption methods. Therefore, in this work, we have investigated the comparison of the extraction yield of chlorophylls and carotenoids from the wet and heat-dried microalgal biomass (isolated Chlorella thermophila) using ethanol. Extraction parameters such as homogenisation time, homogenisation speed, solvent temperature, solid-solvent ratio, boiling time and microwave time have been optimised. Chlorophyll extraction yield was observed to be 2.7 fold higher from wet biomass than dry biomass while carotenoid yield was 6.7 fold higher. Highest chlorophyll yield (∼60 mg/g-dry biomass) was observed at 6 min of homogenisation time, 10,000 rpm, solid solvent ratio of 1 mg/mL and 58 °C of solvent temperature from wet biomass with extraction efficiency of ∼94 %. Highest carotenoid yield was noticed following the same conditions of chlorophyll extraction except 4 °C of solvent temperature. The modelling of the extraction process was performed using artificial neural network (ANN) which may be useful for the scale-up of the extraction process at the industrial level. [ABSTRACT FROM AUTHOR] |
| Copyright of Process Biochemistry is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 144583819 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Extraction of chlorophylls and carotenoids from dry and wet biomass of isolated Chlorella Thermophila: Optimization of process parameters and modelling by artificial neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sarkar%2C+Sambit%22">Sarkar, Sambit</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Manna%2C+Mriganka+Sekhar%22">Manna, Mriganka Sekhar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bhowmick%2C+Tridib+Kumar%22">Bhowmick, Tridib Kumar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gayen%2C+Kalyan%22">Gayen, Kalyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kalyan.chemical@nita.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Process+Biochemistry%22">Process Biochemistry</searchLink>. Sep2020, Vol. 96, p58-72. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Carotenoids%22">Carotenoids</searchLink><br /><searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Chlorella%22">Chlorella</searchLink><br /><searchLink fieldCode="DE" term="%22Chlorophyll%22">Chlorophyll</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Biomass%22">Biomass</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • Extraction of chlorophylls and carotenoids from microalgae using ethanol • Chlorophyll yield from wet biomass was 2.7 fold higher than from dry biomass • Extraction efficiency of ∼90 % was achieved by high speed homogenisation • Boiling and microwave in combination with homogenisation were inefficient • Modelling of the extraction process using Artificial Neural Network (ANN) Chlorophylls and carotenoids can be extracted from microalgae using various solvents. However, there is lack of studies regarding the comparison of extraction yield of these pigments from wet and dry microalgal biomass using different combination of cell disruption methods. Therefore, in this work, we have investigated the comparison of the extraction yield of chlorophylls and carotenoids from the wet and heat-dried microalgal biomass (isolated Chlorella thermophila) using ethanol. Extraction parameters such as homogenisation time, homogenisation speed, solvent temperature, solid-solvent ratio, boiling time and microwave time have been optimised. Chlorophyll extraction yield was observed to be 2.7 fold higher from wet biomass than dry biomass while carotenoid yield was 6.7 fold higher. Highest chlorophyll yield (∼60 mg/g-dry biomass) was observed at 6 min of homogenisation time, 10,000 rpm, solid solvent ratio of 1 mg/mL and 58 °C of solvent temperature from wet biomass with extraction efficiency of ∼94 %. Highest carotenoid yield was noticed following the same conditions of chlorophyll extraction except 4 °C of solvent temperature. The modelling of the extraction process was performed using artificial neural network (ANN) which may be useful for the scale-up of the extraction process at the industrial level. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Process Biochemistry is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.procbio.2020.05.025 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 58 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Carotenoids Type: general – SubjectFull: Process optimization Type: general – SubjectFull: Chlorella Type: general – SubjectFull: Chlorophyll Type: general – SubjectFull: Manufacturing processes Type: general – SubjectFull: Biomass Type: general Titles: – TitleFull: Extraction of chlorophylls and carotenoids from dry and wet biomass of isolated Chlorella Thermophila: Optimization of process parameters and modelling by artificial neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sarkar, Sambit – PersonEntity: Name: NameFull: Manna, Mriganka Sekhar – PersonEntity: Name: NameFull: Bhowmick, Tridib Kumar – PersonEntity: Name: NameFull: Gayen, Kalyan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 13595113 Numbering: – Type: volume Value: 96 Titles: – TitleFull: Process Biochemistry Type: main |
| ResultId | 1 |
