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Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network
An Artificial Neural Network (ANN) based on the Quick Propagation (QP) algorithm was used in conjunction with an experimental design to optimize the lipase-catalyzed reaction conditions for the preparation of a triethanolamine (TEA)-based esterquat cationic surfactant. Using the best performing ANN,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264229/ https://www.ncbi.nlm.nih.gov/pubmed/21716175 http://dx.doi.org/10.3390/molecules16075538 |
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author | Masoumi, Hamid Reza Fard Kassim, Anuar Basri, Mahiran Abdullah, Dzulkifly Kuang Haron, Mohd Jelas |
author_facet | Masoumi, Hamid Reza Fard Kassim, Anuar Basri, Mahiran Abdullah, Dzulkifly Kuang Haron, Mohd Jelas |
author_sort | Masoumi, Hamid Reza Fard |
collection | PubMed |
description | An Artificial Neural Network (ANN) based on the Quick Propagation (QP) algorithm was used in conjunction with an experimental design to optimize the lipase-catalyzed reaction conditions for the preparation of a triethanolamine (TEA)-based esterquat cationic surfactant. Using the best performing ANN, the optimum conditions predicted were an enzyme amount of 4.77 w/w%, reaction time of 24 h, reaction temperature of 61.9 °C, substrate (oleic acid: triethanolamine) molar ratio of 1:1 mole and agitation speed of 480 r.p.m. The relative deviation percentage under these conditions was less than 4%. The optimized method was successfully applied to the synthesis of the TEA-based esterquat cationic surfactant at a 2,000 mL scale. This method represents a more flexible and convenient means for optimizing enzymatic reaction using ANN than has been previously reported by conventional methods. |
format | Online Article Text |
id | pubmed-6264229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62642292018-12-10 Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network Masoumi, Hamid Reza Fard Kassim, Anuar Basri, Mahiran Abdullah, Dzulkifly Kuang Haron, Mohd Jelas Molecules Article An Artificial Neural Network (ANN) based on the Quick Propagation (QP) algorithm was used in conjunction with an experimental design to optimize the lipase-catalyzed reaction conditions for the preparation of a triethanolamine (TEA)-based esterquat cationic surfactant. Using the best performing ANN, the optimum conditions predicted were an enzyme amount of 4.77 w/w%, reaction time of 24 h, reaction temperature of 61.9 °C, substrate (oleic acid: triethanolamine) molar ratio of 1:1 mole and agitation speed of 480 r.p.m. The relative deviation percentage under these conditions was less than 4%. The optimized method was successfully applied to the synthesis of the TEA-based esterquat cationic surfactant at a 2,000 mL scale. This method represents a more flexible and convenient means for optimizing enzymatic reaction using ANN than has been previously reported by conventional methods. MDPI 2011-06-29 /pmc/articles/PMC6264229/ /pubmed/21716175 http://dx.doi.org/10.3390/molecules16075538 Text en © 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Masoumi, Hamid Reza Fard Kassim, Anuar Basri, Mahiran Abdullah, Dzulkifly Kuang Haron, Mohd Jelas Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network |
title | Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network |
title_full | Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network |
title_fullStr | Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network |
title_full_unstemmed | Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network |
title_short | Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network |
title_sort | multivariate optimization in the biosynthesis of a triethanolamine (tea)-based esterquat cationic surfactant using an artificial neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264229/ https://www.ncbi.nlm.nih.gov/pubmed/21716175 http://dx.doi.org/10.3390/molecules16075538 |
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