Modeling of Dispersive Liquid-Liquid Microextraction for Determination of Essential Oil from Borago officinalis L. By Using Combination of Artificial Neural Network and Genetic Algorithm Method

Abstract

Dispersive liquid-liquid microextraction (DLLME) coupled with gas chromatography was applied for the extraction and determination of essential oil constituents of the Borago officinalis L. In this study, an experimental data-based artificial neural network (ANN) model was constructed to describe the performance of DLLME method for various operating conditions. The volume of extraction and dispersive solvents, extraction time and salt effect were the input variables of this process, whereas the extraction efficiency was the output. The ANN method was found to be capable of modeling this procedure accurately. The overall agreement between the experimental data and ANN predictions was satisfactory showing a determination coefficient of 0.982. The optimum operating condition was then determined by the genetic algorithm method. The optimal conditions were 248 μL volume of extraction solvent, 260 μL volume of dispersive solvent, 2.5 min extraction time and 0.16 mol L-1 of salt. The limit of detection and linear dynamic range were 0.15-24.0 and 1.2-1,800 ng mL-1, respectively. The main components of the essential oil were δ-cadinene (31.02%), carvacrol (24.91%), α-pinene (20.89%) and α-cadinol (16.47%).