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Updated: May 31, 2026

Self-Nanoemulsification of Healthy Oils to Enhance the Solubility of Lipophilic Drugs
Published on: July 27, 2022
Multivariate optimization in the biosynthesis of a triethanolamine (TEA)-based esterquat cationic surfactant using an
Hamid Reza Fard Masoumi1, Anuar Kassim, Mahiran Basri
1Department of Chemistry, Faculty of Science, University Putra Malaysia, 43400 Selangor, Malaysia.
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.
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