Multivariate optimization of a GC–MS method for determination of sixteen priority polycyclic aromatic hydrocarbons in environmental samples

Abstract

This paper describes the development and optimization, by using multivariate analysis, of a GC–MS-SIM method for evaluation of the 16 polyaromatic hydrocarbons considered as priority pollutants in atmospheric particulate material by the US EPA. In order to assure an adequate separation in the shortest analysis time, a multivariate design was used to set the conditions of the oven temperature program. The optimization process was carried out using factorial fractional design and Box–Behnken design. The following factors were evaluated: initial temperature, temperature rate #1, intermediary temperature, temperature rate #2, and final temperature. The optimized conditions were set at: 70°C (2 min) → 200°C (30°C/min, 5 min) → 300°C (5°C/min, 1.67 min). Moreover, we have also optimized the injector temperature as 310°C and sampling time as 0.8 min. The total analysis time was 33 min. Validation of GC–MS-SIM yielded satisfactory results for repetitivity of the detector response and retention times, and linearity of calibration curves. LOD were established as 0.13–0.34 ng/mL (peak area) and 0.18–0.72 ng/mL (peak height). The method has been shown to be appropriate for the analysis of samples of atmospheric particulate material and/or other environmental matrices.