Electrochemical fingerprints of brominated trihaloacetic acids (HAA3) mixtures in water0

被引:16
作者
Ceto, Xavier [1 ]
Saint, Christopher [2 ]
Chow, Christopher W. K. [2 ,3 ]
Voelcker, Nicolas H. [4 ]
Prieto-Simon, Beatriz [1 ]
机构
[1] Univ South Australia, Future Ind Inst, Adelaide, SA 5095, Australia
[2] Univ South Australia, Sch Nat & Built Environm, Nat & Built Environm Res Ctr, Mawson Lakes, SA 5095, Australia
[3] SA Water, Australian Water Qual Ctr, Adelaide, SA 5000, Australia
[4] Monash Univ, Monash Inst Pharmaceut Sci, Parkville, Vic 3052, Australia
基金
澳大利亚研究理事会;
关键词
Haloacetic acids; Disinfection by-products; Electronic tongue; Artificial neural networks; Principal component analysis; DISINFECTION BY-PRODUCTS; DRINKING-WATER; ION CHROMATOGRAPHY; MASS-SPECTROMETRY; HALOACETIC ACIDS; CONTAMINANTS; GENOTOXICITY; TONGUES; TRENDS;
D O I
10.1016/j.snb.2017.02.179
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In this work, we explore the capabilities of combining electrochemical sensors and chemometrics towards the analysis of haloacetic acids (HAAs) in water samples. Our approach is based on electronic tongue principles. It combines voltammetric measurements on a gold electrode with chemometric data processing, to extract characteristic fingerprints for HAAs. Cyclic and square wave voltammograms were pre-processed by means of fast Fourier transform (FFT) to provide the coefficients used as subsequent inputs for an artificial neural network (ANN) model. We were able to quantitatively detect and discriminate each HAA under study. Quantitation of HAA3 mixtures (i.e. bromodichloroacetic acid, dibromochloroacetic acid and tribromoacetic acid) was achieved at the vg/L level, with a normalized root mean square error (NRMSE) of 0.054 for the validation subset. Finally, successful analysis of spiked water samples was achieved demonstrating the operation of the sensor in the absence of matrix effects. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:70 / 77
页数:8
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