COMBINING ARTIFICIAL NEURAL NETWORK AND MOTH-FLAME OPTIMIZATION ALGORITHM FOR OPTIMIZATION OF ULTRASOUND-ASSISTED AND MICROWAVE-ASSISTED EXTRACTION PARAMETERS: BARK OF Pinus brutia

Abstract

In this study, the extraction parameters of Pinus brutia bark were optimized using a hybrid artificial intelligence technique. Firstly, the bark samples were extracted by ultrasound-assisted extraction and microwave-assisted extraction which are defined as 'green' extraction methods at different conditions. The selected extraction parameters for ultrasound-assisted extraction were 0:100; 20:80; 40:60; 80:20 (%) ethanol: water ratios; 40 degrees C, 60 degrees C extraction temperatures and 5 min, 10 min, 15 min, 20 min extraction times and for microwave-assisted extraction were 90, 180, 360, 600, 900 (W) microwave power, 0:100; 20:80; 40:60; 60:40; 80:20 (%) ethanol: water ratios. Then Stiasny number, condensed tannin content and reducing sugar content of all extracts were determined. Next, the prediction models were developed for each studied parameter using Artificial Neural Network. Finally, the extraction parameters were optimized using Moth-Flame Optimization Algorithm. After that optimization process, while the extraction time was the same (5 min), the ethanol: water ratio and extraction temperature values differed for the optimization of all studied assays of ultrasound-assisted extraction. Also, microwave power and ethanol: water ratio variables were found in different values for each assay of microwave-assisted extraction. The results showed that the Artificial Neural Network and Moth-Flame Optimization could be a novel and powerful hybrid approach to optimize the extraction parameters of Pinus brutia barks with saving time, cost, chemical and effort.