Research Progress of Analysis of Mineral Oil Hydrocarbons Using On-Line High Performance Liquid Chromatography Coupled with Gas Chromatography

In the past decades, the application of on-line high performance liquid chromatography coupled with gas chromatography (HPLC-GC) has rapidly developed in quantitative analysis of mineral oil hydrocarbons (MOH). MOH are derived from petroleum and synthetic fuels and cover a large range of boiling points, mainly consisting of mineral oil saturated hydrocarbons (MOSH) and aromatic hydrocarbons (MOAH). Usually, MOH are migrated from variety of sources to various foods, thus existing in foods widely, which have been an adverse impact on human health. However, the analysis of MOH is challenging due to the limited selectivity and sensitivity of hydrogen flame ionization detector (FID). The on-line HPLC-GC combines high separation efficiency of HPLC and equal-carbon-response detection of GC-FID, which solves the problem of selectivity and sensitivity of MOH analysis. The innovation of retention gap, Y-piece interface and solvent vapor exit equipped in HPLC-GC employ large volume injection into GC-FID. Since the elution fractions from HPLC are completely transferred to GC, the analytical sensitivity of MOH is enhanced by more than two orders of magnitude and the contaminants introduced are minimized during the analytical procedure, ensuring reproducibility and accuracy of the MOH analysis. In this paper, the research history of interfaces used in on-line HPLC-GC and the matching conditions of HPLC and GC were described in detail and the sample preparation methods of interference removal and MOH enrichment based on HPLC-GC were reviewed. The limitations of HPLC-GC were summarized and the future application of the technology and the development trend in MOH analysis were prospected in the end.

Automated summary

The combination of on-line high performance liquid chromatography and gas chromatography (HPLC-GC) has rapidly developed in quantitative analysis of mineral oil hydrocarbons (MOH), solving the challenges of selectivity and sensitivity in MOH analysis, enhancing analytical sensitivity, and ensuring reproducibility and accuracy in the analysis process.