Esters in the Food and Cosmetic Industries: An Overview of the Reactors Used in Their Biocatalytic Synthesis

Abstract

Esters are versatile compounds with a wide range of applications in various industries due to their unique properties and pleasant aromas. Conventionally, the manufacture of these compounds has relied on the chemical route. Nevertheless, this technique employs high temperatures and inorganic catalysts, resulting in undesired additional steps to purify the final product by removing solvent residues, which decreases environmental sustainability and energy efficiency. In accordance with the principles of “Green Chemistry” and the search for more environmentally friendly methods, a new alternative, the enzymatic route, has been introduced. This technique uses low temperatures and does not require the use of solvents, resulting in more environmentally friendly final products. Despite the large number of studies published on the biocatalytic synthesis of esters, little attention has been paid to the reactors used for it. Therefore, it is convenient to gather the scattered information regarding the type of reactor employed in these synthesis reactions, considering the industrial field in which the process is carried out. A comparison between the performance of the different reactor configurations will allow us to draw the appropriate conclusions regarding their suitability for each specific industrial application. This review addresses, for the first time, the above aspects, which will undoubtedly help with the correct industrial implementation of these processes.

Latest Trends in Lipase-Catalyzed Synthesis of Ester Carbohydrate Surfactants: From Key Parameters to Opportunities and Future Development

“…The exponential increase in knowledge about enzymes, combined with technological and biological advances, means that complex genomic and proteomic databases can be created. This data can then be processed by artificial intelligence (AI), with predictive functional analysis, imposing discriminating criteria, to perform screening [177,178]. Simply considering machine learning, which is a subset of AI, 2 approaches are possible for predicting a synthesis [179]: non-supervised learning and supervised one.…”