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Isotope pattern deconvolution as a successful alternative to calibration curve for application in wastewater-based epidemiology

机译:Isotope pattern deconvolution as a successful alternative to calibration curve for application in wastewater-based epidemiology

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摘要

An isotope pattern deconvolution (IPD) quantification method has been applied for the determination of five substances (amphetamine, benzoylecgonine, cocaine, methamphetamine and MDMA) in wastewater for the application in wastewater-based epidemiology (WBE). A previously validated method that used a calibration curve for quantification was modified to apply IPD. The two approaches were compared in terms of analytical uncertainty in recovery studies of quality control samples, i.e. six wastewater samples from different geographical origins spiked at two concentration levels. Both methods were reliable as they passed (z-score < 2) in an interlaboratory exercise. After 60 individual determinations. IPD provided 11 results outside recovery limits (70-120%) while the previous method produced 31 adverse results. All mean values for IPD were accurate whereas 6 out of 10 results showed RSD values higher than 30% or recoveries outside limits when using the former method. Moreover, the calculated method bias for the latter doubles that of IPD, which, in turn, makes the combined uncertainty (u(c)) much higher. Consequently, a simple change of data treatment-IPD quantification methodology-resulted in a lower uncertainty of the estimated illicit drug concentration, one of the main steps contributing to the final uncertainty in the normalized daily drug consumption through WBE. The current study demonstrated that the employment of IPD can also be very interesting for future applications of WBE, especially when matrix effects are high, complicating accurate quantification. In addition, when a high number of samples and/or compounds need to be analysed, IPD is faster than calibration and, eventually, cost-effective when isotopically labelled internal standard is highly expensive.

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