Combining artificial neural network and moth-flame optimization algorithm for optimization of ultrasound-assisted and microwave-assisted extraction parameters: Bark of Pinus brutia

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