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).

Guo Zhao,Zhiqiang Wang,Gang Liu,Hui Wang

doi:10.3390/s16091540

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.

Highlights

Lead is regarded as one of the most toxic heavy metals in the environment [1]
A back propagation artificial neural network (BP-Artificial neural networks (ANNs)) have been applied to the optimization of a stripping voltammetric sensor and the analysis of the square wave anodic stripping voltammetry (SWASV) spectrum for the prediction of Pb(II) within the range of 1–110 μg/L
To the best of our knowledge, this is the first report of the combination of SWASV and the BP-ANN model using Bi/glassy carbon electrode (Bi/GCE) to predict the concentration of Pb(II) in the presence of Cd(II)

Summary

Introduction

Lead is regarded as one of the most toxic heavy metals in the environment [1]. It can pose acute or chronic risks to ecosystem at very low concentrations because of their high toxicities, high stabilities and propagated accumulation through food chains [2]. This is to say, from plants grown in tainted agricultural land or meat from animals which have ingested plants grown in contaminated soil [3]. In ASV [5] metal

Methods

Discussion

Conclusion