Application of Artificial Neural Network and Response Surface Methodology in Optimization of Cold Fiber Headspace Solid-Phase Microextraction (CF-HS-SPME) for Analysis of 2, 5-Hexandion in Urine Samples

Abstract

Background: Cold-Fiber Solid-Phase Microextraction (CF-SPME) is often used for the extraction of volatile and semi-volatile compounds from complex matrices. Multivariate statistical optimization techniques can save time and chemicals and thus decrease the analytical cost in comparison with the Single Variable Approach (SVA). Over the past few decades, different beneficial mathematical tools have been developed for the optimization of separation processes. Objectives: In this study, the Artificial Neural Network (ANN) and Response Surface Methodology (RSM) applications were compared for CF-SPME optimization to determine 2, 5-hexandion in urine samples. 2, 5-Hexanedione is a colorless liquid and the main metabolite of n-hexane as a result of occupational exposure. Methods: N-hexane is widely used in the rubber industry, food processing, solvents, and medicinal drugs and has adverse effects such as neurotoxicity on humans. Thus, biological monitoring, analytic methods, and mathematical and statistical techniques concerning 2.5-HD are very important. The RSM and ANN are mathematical and statistical techniques applied for the optimization and process modeling. Designing an experiment based on the Historical Data Design (HDD) of RSM was adopted to evaluate the relationship between independent parameters such as extraction temperature, extraction time, sample volume, and extraction efficiency of 2, 5-hexandion. Results: The models were compared for their predictive ability by the coefficient of determination (R2) and Root Mean Square Error (RSME) based on the train and test dataset. Conclusions: The results were highly significant (P < 0.05) for the optimization of variables. The ANN model had more generalizability than the RSM model. Also, the ANN had higher predictive accuracy.