Multi-response optimization followed by multivariate calibration for simultaneous determination of carcinogenic polycyclic aromatic hydrocarbons in environmental samples using gold nanoparticles

Abstract

In this study, a multivariate-based strategy was developed for simultaneous determination of thirteen carcinogenic polycyclic aromatic hydrocarbons (PAHs) in water samples using gold nanoparticles (AuNPs) as solid-phase extraction (SPE) sorbent combined with gas chromatography (GC). The extraction technique is based on the strong affinity between citrate-capped AuNPs and PAHs. Furthermore, characterization of AuNPs was performed by UV-vis spectroscopy and transmission electron microscopy (TEM) techniques. A rotatable central composite design (CCD) combined with multiple linear regression (MLR) was used for designing the extraction procedure and developing models using the GC peak areas of 13 PAHs. Moreover, multi-response optimization using the Derringer desirability function was utilized to find optimum conditions, which were 7.22 min adsorption vortex time, 5 μL of 1,3-propanedithiol as desorption solvent, 44 μL methanol, 15 μL n-nonane as acceptor solvent and 9.63 min desorption vortex time. The optimized method was then used for identification and quantification of target PAHs in standard and spiked samples using partial least squares regression (PLSR). Different variable selection methods including PLS regression vector (RV), variable importance in projection (VIP) and selectivity ratio (SR) were tested and RV showed the best performance. Finally, the proposed strategy was successfully tested for the analysis of spiked water samples (i.e., from tap, well and farm).