Updating the on the extraction of bioactive compounds in agro-products using a two-pot multivariate design. A comprehensive review.

Extraction is regarded as the most crucial stage in analyzing bioactive compounds. Nonetheless, due to the intricacy of the matrix, numerous aspects must be optimized during the extraction of bioactive components. Although one variable at a time (OVAT) is mainly used, this is time-consuming and laborious. As a result, using an experimental design in the optimization process is beneficial with few experiments and low costs. This article critically reviewed two-pot multivariate techniques employed in extracting bioactive compounds in food in the last decade. First, a comparison of the parametric screening methods (factorial design, Taguchi, and Plackett-Burman design) was delved into, and its advantages and limitations in helping to select the critical extraction parameters were discussed. This was followed by a discussion of the response surface methodologies (central composite (CCD), Doehlert (DD), orthogonal array (OAD), mixture, D-optimal, and Box-Behnken designs (BBD), .), which are used to optimize the most critical variables in the extraction of bioactive compounds in food, providing a sequential comprehension of the linear and complex interactions and multiple responses and robustness tests. Next, the benefits, drawbacks, and possibilities of various response surface methodologies (RSM) and some of their usages were discussed, with food chemistry, analysis, and processing from the literature. Finally, extraction of food bioactive compounds using RSM was compared to artificial neural network modeling with their drawbacks discussed. We recommended that future experiments could compare these designs (BBD CCD DD, .) in the extraction of food-bioactive compounds. Besides, more research should be done comparing response surface methodologies and artificial neural networks regarding their practicality and limitations in extracting food-bioactive compounds.