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Sensors 2016, 16(9), 1540; doi:10.3390/s16091540

Optimization of Stripping Voltammetric Sensor by a Back Propagation Artificial Neural Network for the Accurate Determination of Pb(II) in the Presence of Cd(II)

1,2
,
1,2
,
1,2,* and 3
1
Key Lab of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing 100083, China
2
Key Lab of Agricultural Information Acquisition Technology, Ministry of Agricultural of China, China Agricultural University, Beijing 100083, China
3
College of Computer Science and Technology, Shandong University of Technology, Zibo 255049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Russell Binions
Received: 29 July 2016 / Revised: 29 August 2016 / Accepted: 7 September 2016 / Published: 21 September 2016
(This article belongs to the Special Issue Sensors for Environmental Monitoring 2016)
View Full-Text   |   Download PDF [3411 KB, uploaded 21 September 2016]   |  

Abstract

An easy, but effective, method has been proposed to detect and quantify the Pb(II) in the presence of Cd(II) based on a Bi/glassy carbon electrode (Bi/GCE) with the combination of a back propagation artificial neural network (BP-ANN) and square wave anodic stripping voltammetry (SWASV) without further electrode modification. The effects of Cd(II) in different concentrations on stripping responses of Pb(II) was studied. The results indicate that the presence of Cd(II) will reduce the prediction precision of a direct calibration model. Therefore, a two-input and one-output BP-ANN was built for the optimization of a stripping voltammetric sensor, which considering the combined effects of Cd(II) and Pb(II) on the SWASV detection of Pb(II) and establishing the nonlinear relationship between the stripping peak currents of Pb(II) and Cd(II) and the concentration of Pb(II). The key parameters of the BP-ANN and the factors affecting the SWASV detection of Pb(II) were optimized. The prediction performance of direct calibration model and BP-ANN model were tested with regard to the mean absolute error (MAE), root mean square error (RMSE), average relative error (ARE), and correlation coefficient. The results proved that the BP-ANN model exhibited higher prediction accuracy than the direct calibration model. Finally, a real samples analysis was performed to determine trace Pb(II) in some soil specimens with satisfactory results. View Full-Text
Keywords: square wave anodic stripping voltammetry; artificial neural network; bismuth film electrode; Pb(II); Cd(II) square wave anodic stripping voltammetry; artificial neural network; bismuth film electrode; Pb(II); Cd(II)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, G.; Wang, H.; Liu, G.; Wang, Z. Optimization of Stripping Voltammetric Sensor by a Back Propagation Artificial Neural Network for the Accurate Determination of Pb(II) in the Presence of Cd(II). Sensors 2016, 16, 1540.

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