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Journal of Spectroscopy
Volume 2016 (2016), Article ID 3597451, 7 pages
Research Article

Combining Near-Infrared Spectroscopy and Chemometrics for Rapid Recognition of an Hg-Contaminated Plant

1Research Institute of Applied Chemistry, College of Material and Chemical Engineering, Tongren University, Tongren, Guizhou 554300, China
2The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, College of Pharmacy, South-Central University for Nationalities, Wuhan 430074, China
3College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China

Received 10 March 2016; Revised 29 April 2016; Accepted 17 May 2016

Academic Editor: Paulo R. G. Hein

Copyright © 2016 Bang-Cheng Tang et al. 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.


The feasibility of rapid recognition of an Hg-contaminated plant as a soil pollution indicator was investigated using near-infrared spectroscopy (NIRS) and chemometrics. The stem and leave of a native plant, Miscanthus floridulus (Labill.) Warb. (MFLW), were collected from Hg-contaminated areas () as well as from regular areas (). The samples were dried and crushed and the powders were sieved through an 80-mesh sieve. Reference analysis of Hg levels was performed using inductively coupled plasma-atomic emission spectrometry (ICP-AES). The actual Hg contents of contaminated and normal samples were 16.2–30.5 and 0.0–0.1 mg/Kg, respectively. The NIRS measurements of impacted sample powders were collected in the mode of reflectance. The DUPLEX algorithm was utilized to split the NIRS data into representative training and test sets. Different spectral preprocessing methods were performed to remove the unwanted and noncomposition-correlated spectral variations. Classification models were developed using partial least squares discrimination analysis (PLSDA) based on the raw, smoothed, second-order derivative (D2), and standard normal variate (SNV) data, respectively. The prediction accuracy obtained by PLSDA with each data preprocessing option was 100%, indicating pattern recognition of Hg-contaminated MFLW samples using NIRS data was in perfect consistence with the ICP-AES results. NIRS combined with chemometrics will provide a tool to screen the Hg-contaminated MFLW, which can be potentially used as an indicator of soil pollution.