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A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine.
Journal of Analytical Methods in Chemistry ( IF 2.6 ) Pub Date : 2019-09-09 , DOI: 10.1155/2019/3240126
Lu Xu 1 , Qiong Shi 2 , Bang-Cheng Tang 1 , Shunping Xie 3
Affiliation  

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.

中文翻译:

傅里叶变换近红外光谱结合最小二乘支持向量机开发的土壤中汞新植物指标(青蒿)。

通过使用傅里叶变换近红外(FT-NIR)光谱结合最小二乘支持向量机(LS)融合,建立了一种通常使用汞矿区中分布的植物(蒿属植物lavandulaefolia DC。,ALDC)对土壤中汞的快速指示剂。 -SVM)。ALDC的代表性样品(茎和叶)是从汞矿的周围和远处采集的。作为参考方法,可通过直接汞分析仪测定土壤和ALDC样品中的总汞含量,该分析仪结合了高温分解,除杂的催化吸附,汞齐化捕集和原子吸收光谱(AAS)。基于ALDC样品的FT-NIR数据,建立了LS-SVM模型以区分受汞污染的土壤和普通土壤。参考分析结果表明,汞矿周围地区的汞水平(0-3公里,7.52-88.59 mg / kg)显着高于远离汞矿地区的汞水平(> 5公里,0-0.75 mg / kg)。 /公斤)。分别基于原始光谱,平滑光谱,二阶导数(D2)光谱和标准正态变换(SNV)光谱建立了ALDC样本的LS-SVM分类模型。D2-LS-SVM的预测精度最高(0.950)。FT-NIR与LS-SVM建模相结合可以快速,准确地识别受污染的ALDC。与依靠肉眼观察植物的传统方法相比,该方法客观,灵敏,适用。59 mg / kg)显着高于远离汞矿的地区(> 5公里,0-0.75 mg / kg)。分别基于原始光谱,平滑光谱,二阶导数(D2)光谱和标准正态变换(SNV)光谱建立了ALDC样本的LS-SVM分类模型。D2-LS-SVM的预测精度最高(0.950)。FT-NIR与LS-SVM建模相结合可以快速,准确地识别受污染的ALDC。与依靠肉眼观察植物的传统方法相比,该方法客观,灵敏,适用。59 mg / kg)显着高于远离汞矿的地区(> 5公里,0-0.75 mg / kg)。分别基于原始光谱,平滑光谱,二阶导数(D2)光谱和标准正态变换(SNV)光谱建立了ALDC样本的LS-SVM分类模型。D2-LS-SVM的预测精度最高(0.950)。FT-NIR与LS-SVM建模相结合可以快速,准确地识别受污染的ALDC。与依靠肉眼观察植物的传统方法相比,该方法客观,灵敏,适用。和标准正变换(SNV)光谱。D2-LS-SVM的预测精度最高(0.950)。FT-NIR与LS-SVM建模相结合可以快速,准确地识别受污染的ALDC。与依靠肉眼观察植物的传统方法相比,该方法客观,灵敏,适用。和标准正变换(SNV)光谱。D2-LS-SVM的预测精度最高(0.950)。FT-NIR与LS-SVM建模相结合可以快速,准确地识别受污染的ALDC。与依靠肉眼观察植物的传统方法相比,该方法客观,灵敏,适用。
更新日期:2019-09-09
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