Huang Li
The synthesis of retinyl laurate, a valuable nutraceutical compound, using lipase-catalyzed esterification is a promising approach. However, traditional methods suffer from low efficiency and yield. In this article, we propose a novel method that integrates artificial neural network (ANN) optimization with ultrasound support to enhance the lipase-catalyzed synthesis of retinyl laurate. We discuss the advantages of this approach, its potential applications in the food and pharmaceutical industries, and future research directions. Retinyl laurate is a compound derived from vitamin A (retinol) and lauric acid, known for its potential health benefits in skin care and nutrition. Traditional chemical synthesis of retinyl laurate is inefficient and involves the use of toxic reagents. Lipase-catalyzed esterification offers a more sustainable and efficient approach for the synthesis of retinyl laurate. However, the efficiency of this process can be further enhanced by optimizing reaction conditions. In this article, we propose a novel method that integrates ANN optimization with ultrasound support to improve the lipase-catalyzed synthesis of retinyl laurate.
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