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Molecules 2011, 16(7), 5538-5549; doi:10.3390/molecules16075538

Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network

Department of Chemistry, Faculty of Science, University Putra Malaysia, 43400, Selangor, Malaysia
Author to whom correspondence should be addressed.
Received: 21 April 2011 / Revised: 13 June 2011 / Accepted: 14 June 2011 / Published: 29 June 2011
Download PDF [273 KB, 18 June 2014; original version 18 June 2014]


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.
Keywords: neural network; optimization; esterquat; enzyme; synthesis neural network; optimization; esterquat; enzyme; synthesis
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Masoumi, H.R.F.; Kassim, A.; Basri, M.; Abdullah, D.K.; Haron, M.J. Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network. Molecules 2011, 16, 5538-5549.

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