title: Multivariate optimization in the biosynthesis of a triethanolamine (TEA)-based esterquat cationic surfactant using an artificial neural network.
abstract: 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.
authors.affiliation: Department of Chemistry, Faculty of Science, University Putra Malaysia, 43400 Selangor, Malaysia.
authors.forename: Hamid Reza Fard
authors.initials: HR
authors.lastname: Masoumi
authors.forename: Anuar
authors.initials: A
authors.lastname: Kassim
authors.forename: Mahiran
authors.initials: M
authors.lastname: Basri
authors.forename: Dzulkifly Kuang
authors.initials: DK
authors.lastname: Abdullah
authors.forename: Mohd Jelas
authors.initials: MJ
authors.lastname: Haron
mesh_terms: D004983:Ethanolamines / Q000737:chemistry*; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D013501:Surface-Active Agents / Q000138:chemical synthesis*
chemical_list: D004983:Ethanolamines; D013501:Surface-Active Agents; C009546:triethanolamine