Open AccessA New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine
Author(s) -
Lu Xu,
Qiong Shi,
BangCheng Tang,
Shunping Xie
Publication year - 2019
Publication title -
journal of analytical methods in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.407
H-Index - 25
eISSN - 2090-8865
pISSN - 2090-8873
DOI - 10.1155/2019/3240126
Subject(s) - fourier transform infrared spectroscopy , mercury (programming language) , fourier transform , fourier transform spectroscopy , infrared , spectroscopy , chemistry , materials science , environmental science , analytical chemistry (journal) , environmental chemistry , physics , mathematics , computer science , optics , mathematical analysis , quantum mechanics , programming language
A rapid indicator of mercury in soil using a plant ( Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.