Modeling and optimization of ultrasound‐assisted cinnamon extraction process using fuzzy and response surface models

Abstract

AbstractThis work was undertaken to assess and model the influence of ethanol concentration (0–96 v/v %), extraction temperature (40–80 °C) and extraction time (20–60 min) on total phenolic content (TPC) values of ultrasound‐assisted extracts using response surface methodology (RSM) and fuzzy models. Both models exhibited good estimations; however, the outcomes of the fuzzy model were marginally more accurate. The maximization of TPC (149.3 mg GAE/g dry weight) was therefore performed based on fuzzy optimization which corresponded to ethanol concentration of 72%, extraction time of 50 min and temperature of 70 °C. For this reason, the cinnamon extract samples with maximum TPC calculated by using the fuzzy modeling were analyzed and characterized in terms of molecular (FT‐IR) and compositional (HPLC) properties. The FTIR results revealed characteristic bands while HPLC analysis indicated presence of two major components; trans‐cinnamic acid (41 mg/g) and p‐coumaric acid (2 mg/g) contents. These results verified that the fuzzy model is an effectual tool to model and optimize TPC of ultrasound‐assisted cinnamon extract.Practical applicationsIn the food industry, it is always necessary to design and develop novel functional foods, food supplements, or innovative health‐healing products using functional additives or extracts such as phenolic‐rich cinnamon extracts. The employment of novel process engineering techniques like ultrasound technology to extract valuable functional compounds like phenolics increases extraction efficiency and enhances the functionality of such compounds, offering a more cost‐effective process. In this respect, it is of great importance to discover and determine the optimized conditions for processing of phenolics with the maximum phenolic yield. Therefore, employment of predictive methods in different food processes has been regarded as suitable tools to enhance efficiency of the processes and quality of the final product. In this respect, the fuzzy model as a novel predictive analytic tool, along with response surface methodology (RSM) are applied and compared in this study. The fuzzy model established to be more effective prediction tool was applied to model and optimize extraction process parameters with respect to achievement of maximum phenolics yield. Therefore, optimization and estimation of the optimized conditions of phenolic extraction is assumed to improve the quality of the extracted phenolics and to increase performance of the extraction process with the maximum yield by application extraction process parameters.