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Determination of Cr, Zn, As and Pb in Soil by X-Ray Fluorescence Spectrometry Based on a Partial Least Square Regression Model

文献类型： 外文期刊

作者： Lu, Anxiang 1 ; Qin, Xiangyang 2 ; Wang, Jihua 1 ; Sun, Jiang 3 ; Zhu, Dazhou 2 ; Pan, Ligang 1 ;

作者机构： 1.Beijing Res Ctr Agri Food Testing & Farmland Moni, Beijing, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Beijing Municipal Station Agro Environm Monitorin, Beijing, Peoples R China

关键词： Heavy metal;Soil;Partial least square regression;X-ray fluorescence spectrometry

期刊名称：COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE IV, PT 1

ISSN： 1868-4238

年卷期： 2011 年 344 卷

页码：

收录情况： SCI

摘要： Soil samples were collected from five provinces over China, including Beijing, Xinjiang, Heilongjiang, Yunnan, and Jiangsu. Heavy metal Cr, Zn, Pb and As in soils were analyzed by a portable X-ray fluorescence spectrometry (XRF). For predicating metal concentration in soils, a partial least square regression model (PLSR) was established. After cross-calibration, the correlation coefficients for validation (R) of value predicted by PLSR model against that measured by AAS and AFS for Cr, Zn, Pb and As was 0.984, 0.929, 0.979, and 0.958, square error of validation (SEP)was 108 mg kg(-1), 117 mg kg(-1), 116 mg kg(-1), and 167 mg kg(-1) for metals concentration from about 100 to 1500 mg kg(-1), and the relative square error of validation(RSEP) was about 14.5 %, 15.6 %, 14.9 %, and 21.0 %. These results indicated XRF based on PLSR model could be applied for determination of Cr, Zn, Pb and As in soil, and would be an effective tool for rapid, quantitative monitoring of metal contamination.

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