Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

Liu, Ning; Zhao, Guo; Liu, Gang

doi:10.3390/s20236792

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Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

Ning Liu

^1,2

Guo Zhao

³ and

Gang Liu

^1,2,*

Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing 100083, China

Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs of China, China Agricultural University, Beijing 100083, China

College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China

Author to whom correspondence should be addressed.

Sensors 2020, 20(23), 6792; https://doi.org/10.3390/s20236792

Received: 10 November 2020 / Revised: 19 November 2020 / Accepted: 25 November 2020 / Published: 27 November 2020

(This article belongs to the Special Issue Sensor Fusion for Precision Agriculture)

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Abstract

In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu²⁺ contents on the SWASV signals of Pb²⁺ was investigated, and a nonlinear relationship between Pb²⁺ concentration and the peak currents of Pb²⁺ and Cu²⁺ was determined. Thus, an SVR model with two inputs (i.e., peak currents of Pb²⁺ and Cu²⁺) and one output (i.e., Pb²⁺ concentration) was trained to quantify the above nonlinear relationship. The SWASV measurement conditions and the SVR parameters were optimized. In addition, the SVR mode, multiple linear regression model, and direct calibration mode were compared to verify the detection performance by using the determination coefficient (R²) and root-mean-square error (RMSE). Results showed that the SVR model with R² and RMSE of the test dataset of 0.9942 and 1.1204 μg/L, respectively, had better detection accuracy than other models. Lastly, real soil samples were applied to validate the practicality and accuracy of the developed method for the detection of Pb²⁺ with approximately equal detection results to the atomic absorption spectroscopy method and a satisfactory average recovery rate of 98.70%. This paper provided a new method for accurately detecting the concentration of heavy metals (HMs) under the interference of non-target HMs for environmental monitoring. View Full-Text

Keywords: lead in soil; interference of copper; support vector regression (SVR); nonlinear relationship; bismuth film-modified electrode

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Liu, N.; Zhao, G.; Liu, G. Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately. Sensors 2020, 20, 6792. https://doi.org/10.3390/s20236792

AMA Style

Liu N, Zhao G, Liu G. Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately. Sensors. 2020; 20(23):6792. https://doi.org/10.3390/s20236792

Chicago/Turabian Style

Liu, Ning, Guo Zhao, and Gang Liu. 2020. "Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately" Sensors 20, no. 23: 6792. https://doi.org/10.3390/s20236792

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Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

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