TY - JOUR
T1 - Simultaneous identification and quantification of nitro-containing explosives by advanced chemometric data treatment of cyclic voltammetry at screen-printed electrodes
AU - Cetó, Xavier
AU - O'Mahony, Aoife M.
AU - Wang, Joseph
AU - Del Valle, Manel
PY - 2013/1/1
Y1 - 2013/1/1
N2 - The simultaneous determination of three nitro-containing compounds found in the majority of explosive mixtures, namely hexahydro-1,3,5-trinitro-1,3,5- triazine (RDX), 2,4,6-trinitrotoluene (TNT) and pentaerythritol tetranitrate (PETN), is demonstrated using both qualitative and quantitative approaches involving the coupling of electrochemical measurements and advanced chemometric data processing. Voltammetric responses were obtained from a single bare screen-printed carbon electrode (SPCE), which exhibited marked mix-responses towards the compounds examined. The responses obtained were then preprocessed employing discrete wavelet transform (DWT) and the resulting coefficients were input to an artificial neural network (ANN) model. Subsequently, meaningful data was extracted from the complex voltammetric readings, achieving either the correct discrimination of the different commercial mixtures (100% of accuracy, sensitivity and specificity) or the individual quantification of each of the compounds under study (total NRMSE of 0.162 for the external test subset). © 2013 Elsevier B.V. All rights reserved.
AB - The simultaneous determination of three nitro-containing compounds found in the majority of explosive mixtures, namely hexahydro-1,3,5-trinitro-1,3,5- triazine (RDX), 2,4,6-trinitrotoluene (TNT) and pentaerythritol tetranitrate (PETN), is demonstrated using both qualitative and quantitative approaches involving the coupling of electrochemical measurements and advanced chemometric data processing. Voltammetric responses were obtained from a single bare screen-printed carbon electrode (SPCE), which exhibited marked mix-responses towards the compounds examined. The responses obtained were then preprocessed employing discrete wavelet transform (DWT) and the resulting coefficients were input to an artificial neural network (ANN) model. Subsequently, meaningful data was extracted from the complex voltammetric readings, achieving either the correct discrimination of the different commercial mixtures (100% of accuracy, sensitivity and specificity) or the individual quantification of each of the compounds under study (total NRMSE of 0.162 for the external test subset). © 2013 Elsevier B.V. All rights reserved.
KW - Artificial neural network
KW - Electronic tongue
KW - Explosives
KW - TNT
KW - Voltammetric sensor
U2 - 10.1016/j.talanta.2012.12.042
DO - 10.1016/j.talanta.2012.12.042
M3 - Article
VL - 107
SP - 270
EP - 276
JO - Talanta
JF - Talanta
SN - 0039-9140
ER -