Application of Graphical Optimization, Desirability, and Multiple Response Functions in the Extraction of Food Bioactive Compounds

Abstract

Currently, statistical experimental design is employed as a quality control method to produce excellent products at the lowest possible cost in food processing, speeding up the process of developing new foods, cutting down costs associated with research, easing the process of moving food products from the research and development stage to the manufacturing stage, and troubleshooting manufacturing issues. In the extraction of bioactive compounds from food, the optimization procedure becomes more straightforward as the optimum conditions of the numerous evaluated responses converge to a similar section. However, the problem becomes more complicated when the optimum values for each response go further apart, making it harder to discover settings that satisfy all of them simultaneously. Using a mathematical function, multiple responses can be integrated into a single one in another manner. The generated response surface enables the calculation of optimum values for extraction parameter, which fulfills all responses whenever possible. Hence, this article reviewed multiple responses, desirability function, and graphical optimization solutions employed to extract food bioactive compounds in the last decade. These techniques’ benefits, drawbacks, potentials, and applications were discussed, including some of their uses in the extraction of bioactive compounds. This will help to address the actual mathematical and statistical issues that arise during the multiple response extraction of food bioactive compounds.

Graphical Abstract