Optimized efficiency of mapping a site contaminated with dioxins by immunoassay compared to gas chromatography-high resolution mass spectrometry.

2016 
The use of an enzyme-linked immunosorbent assay (ELISA) to screen for polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/F) has shown promise as a complementary tool to gas chromatography-high resolution mass spectrometry (GC-HRMS). This is especially true due to its low cost, ease of sample preparation and fast sample turnaround time. One problem that was unaddressed by other research is how to increase the efficiency of ELISA to a point that makes it practical for the analysis of large groups of samples that can have a wide range of unknown PCDD/F concentrations; one ELISA test is unable to screen for PCDD/F concentrations that can range anywhere from background levels to upwards of 10,000 picograms toxic equivalents per gram of soil (pgTEQ g−1). This paper resolves this problem by introducing a sample algorithm which enables the correct amount of dioxin to enter an ELISA tube from a sample (whose unknown PCDD/F concentration can range between 30 and 10,500 pgTEQ g−1) in only two ELISA runs. In doing so, the time and cost benefits of ELISA are preserved. ELISA results for soils and sediment samples processed using the algorithm were then plotted on two site maps alongside their GC-HRMS counterparts. A comparison of both analytical methods showed that areas of high and low PCDD/F concentrations were equally identifiable with either analytical tool; 29 of 32 sample locations on the site maps were placed into the same of three possible screening levels. Therefore, processing ELISA samples through the sample algorithm achieves the necessary level of efficiency while producing virtually equal screening results in comparison to GC-HRMS but at a fraction of the cost. The agreement between GC-HRMS and ELISA was 94 % (R 2 = 0.99, n = 53). GC-HRMS and ELISA results were significantly correlated (Wilcoxon signed rank test p < 0.001).
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    18
    References
    4
    Citations
    NaN
    KQI
    []