Huang Qi-ting, Zhou Lian-qing, Shi Zhou, Li Zhen-yu, Gu Qun
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2009 May;29(5):1434-8.
In the present study, soil samples were scanned by NITON XLt920 field portable X-ray fluorescence (FPXRF) analyzer, and the relationship between the X-ray fluorescence spectra and the concentration of Pb in soil was studied. For predicating the Pb concentration in soil, a partial least square regression model (PLS)was established with 6 optimal factors and two closely relevant electron volt ranges: 10.40-10.70 keV and 12.41-12.80 keV. After cross-calibration, the correlation coefficient of value predicted by PLS model against that measured by ICP was 0.9666, and the root mean square error of prediction (RMSEP) was 0.8732. Meanwhile, the univariate linear regression and multivariate linear regression models were also built with the correlation coefficient of 0.6805 and 0.7302, respectively. Obviously, the PLS method was better than the other two methods for predication. Comparing to the conventional approach of atomic absorption spectroscopy (AAS), FPXRF has the advantages of rapidness, non-destruction and relatively low cost with the acceptable accuracy. It would be a powerful tool to decide which sample is needs for further analysis.
在本研究中，使用NITON XLt920现场便携式X射线荧光（FPXRF）分析仪对土壤样品进行扫描，并研究了X射线荧光光谱与土壤中铅浓度之间的关系。为了预测土壤中的铅浓度，建立了一个偏最小二乘回归模型（PLS），该模型包含6个最优因子以及两个密切相关的电子伏特范围：10.40 - 10.70 keV和12.41 - 12.80 keV。经过交叉校准后，PLS模型预测值与电感耦合等离子体质谱（ICP）测量值的相关系数为0.9666，预测均方根误差（RMSEP）为0.8732。同时，还建立了单变量线性回归模型和多变量线性回归模型，其相关系数分别为0.6805和0.7302。显然，PLS方法在预测方面优于其他两种方法。与传统的原子吸收光谱法（AAS）相比，FPXRF具有快速、无损且成本相对较低的优点，同时具有可接受的准确度。它将成为确定哪些样品需要进一步分析的有力工具。
