alexa A Feasibility Study of NIR Spectra in Identifying Heavy Metal Contamination in Rice Around Abandoned Tailing Ponds: A Case Study inGuiyang County in South China | Abstract
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Journal of Remote Sensing & GIS
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Research Article

A Feasibility Study of NIR Spectra in Identifying Heavy Metal Contamination in Rice Around Abandoned Tailing Ponds: A Case Study inGuiyang County in South China

Ren Hongyan*, Zhuang Dafang, Yang Junxing and Yu Xinfang
State key laboratory of resources and environmental informaiton system, Institute of geographic sciences and natural resources research, CAS, 11A, Datun Road, Chaoyang District, Beijing, China
Corresponding Author : Hongyan Ren
State key laboratory of resources and environmental information system
Beijing 100101, China
Tel: +86-010-64 859025
E-mail: [email protected]
Received June 11, 2013; Accepted September 17, 2013; Published September 22, 2013
Citation: Hongyan R, Dafang Z, Junxing Y, Xinfang Y (2013) A Feasibility Study of NIR Spectra in Identifying Heavy Metal Contamination in Rice Around Abandoned Tailing Ponds: A Case Study in Guiyang County in South China. J Geophys Remote Sensing 2:112. doi:10.4172/2169-0049.1000112
Copyright: © 2013 Hongyan R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Protecting people from heavy metal contamination is an important public-health concern and a major national environmental issue in China. The purpose of this study is to explore the feasibility of the near-infrared (NIR) spectral technique in identifying heavy metal concentration (HMC) in coarse rice. 28 rice samples were gathered from the farmlands around four tailing ponds in Guiyang County of south China, and then were sieved by 2.0 mm plastic mesh for the laboratory spectral measurement and the determination of protein, lead (Pb), and copper (Cu). Before constructing the partial least square regression (PLSR) models for predicting HMC, all spectral data were treated by some methods, including, logarithm (Log), baseline correction (BC), standard normal variate (SNV), multiple scatter correction (MSC), first derivates (FD), and continuum removal (CR). In terms of enrichment coefficients (EC), Pb was accumulated in rice at a high level (17.05). Ä°ts relation to protein (P=0.77, r<0.01) is more significant than that of Cu (P=0.67, r<0.01). Protein content was well predicted by MSC-PLSR model with higher coefficient of determination (R2=0.51) and lower root mean square error (RMSE=0.17%). MSC-PLSR models were respectively built for Pb (R2=0.49, RMSE=2.01 mg/kg) and Cu (R2=0.29, RMSE=0.75 mg/kg). Ä°t is feasible to identify Pb and Cu content in rice by using NIR spectral technique. However, further investigation should be conducted on the application of spectral technique in discriminating the other heavy metals in crops due to the limitations of few samples and particle size interruption.

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