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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

Abstract : 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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Anxiang Lu, Xiangyang Qin, Jihua Wang, Jiang Sun, Dazhou Zhu, et al.. Determination of Cr, Zn, As and Pb in Soil by X-Ray Fluorescence Spectrometry Based on a Partial Least Square Regression Model. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. pp.563-568, ⟨10.1007/978-3-642-18333-1_67⟩. ⟨hal-01559547⟩

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