Ultrasound assisted microwave vacuum drying of persimmon fruit: Modeling by artificial neural network and optimization by genetic algorithm

Abstract

AbstractThe present investigation studied the influence of ultrasonication power (), microwave power (), and vacuum level () on the quality attributes of dried persimmon fruit. The range of independent variables selected for the study was 100–200 W for , 400–800 W for microwave power and 380–680 mmHg of vacuum level. The best artificial neural network with the lowest relative deviation comprised three input neurons, nine hidden neurons, and four output neurons. According to the experimental design, the range of response rehydration ratio , total change in color , drying efficiency (), and antioxidant activity was found to be 1.524%–2.148, 8.933%–28.854, 33.603%–42.155% and 68.640%–87.809% respectively. The optimized condition according to the integrated ANN‐GA model for the process parameters , , and was found to be 106.227 W, 421.189 W, and 668.051 mmHg, respectively.Practical applicationsPersimmon is a perishable fruit with a short shelf life. Fruits necessitate preservation strategies to extend their shelf life by reducing moisture content and water activity. Microwave vacuum drying with ultrasound treatment preserves quality characteristics like color, texture, chemical components, and shrinkage while reducing moisture content. Ultrasound pretreated microwave vacuum drying process lowered drying time while improving the quality and efficiency of dried persimmon fruit. Moisture can rapidly diffuse into the surrounding vacuum and prevent structure collapse due to the high vapor pressure caused by microwave vacuum drying. Prior to drying, sonication alters the structure of the fruit tissue, significantly accelerates the drying process and reduces overall processing time.