科研之友
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摘要
The modelling and prediction of reversed-phase chromatographic retention time (t(R)) under gradient elution conditions for 166 pharmaceuticals in wastewater extracts is presented using artificial neural networks for the first time. Radial basis function, multilayer perceptron and generalised regression neural networks were investigated and a comparison of their predictive ability for model solutions discussed. For real world application, the effect of matrix complexity on t(R) measurements is presented. Measured tit for some compounds in influent wastewater varied by >1 min in comparison to t(R) in model solutions. Similarly, matrix impact on artificial neural network predictive ability was addressed towards developing a more robust approach for routine screening applications. Overall, the best neural network had a predictive accuracy of <1.3 min at the 75th percentile of all measured t(R) data in wastewater samples (<10% of the total runtime). Coefficients of determination for 30 blind test compounds in wastewater matrices lay at or above R-2 = 0.92. Finally, the model Was evaluated for application to the semi-targeted identification of pharmaceutical residues during a weeklong wastewater sampling campaign. The model successfully identified native compounds at a rate of 83 +/- 4% and 73 +/- 5% in influent and effluent extracts, respectively. The use of an FIRMS database and the optimised ANN model was also applied to shortlisting of 37 additional compounds in wastewater. Ultimately, this research will potentially enable faster identification of emerging contaminants in the environment through more efficient post-acquisition data mining.
- 出版日期2015-5-29
