Abstract
This comprehensive review delves into the extensive application of response surface methodology (RSM), a prominent mathematical and statistical technique, for modeling and optimizing the extraction of food-grade bioactive compounds from plant sources. The paper elucidates the optimization approach, covering experimental design, empirical models for response prediction, and the utilization of the desirability function for multiple response optimization. RSM provides a contemporary means to concurrently analyze and optimize various factors, presenting mathematical models for enhancing extraction processes efficiently. The review showcases RSM applications in traditional extraction techniques such as classical solvent extraction, Soxhlet extraction, and hydrodistillation, with a focus on factors like extraction time, temperature, ratio of plant material to solvent, and solvent concentration. The economic feasibility of RSM-optimized extraction processes is discussed, encompassing considerations of processing time, solvent consumption, and overall cost reduction. Critical aspects and challenges related to RSM implementation in extraction optimization are addressed, underscoring the significance of appropriate experimental design, model accuracy, and the incorporation of multiple responses for comprehensive optimization. The review concludes by emphasizing the pivotal role of RSM in guiding rational and efficient extraction processes for various valuable natural compounds from plant materials.