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基于X射线荧光光谱与多特征串联策略的土壤重金属含量预测
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任顺,张雄,任东,杨信廷,张力 1.三峡大学计算机与信息学院2.三峡大学湖北省农田环境监测工程技术研究中心3.农产品质量安全追溯技术及应用国家工程实验室 
基金项目:国家重点研发开发项目(2016YFD0800902);湖北省重大技术创新项目(2017ABA157);农产品质量安全追溯技术及应用国家工程实验室开放基金(GF-NAZS-2019002);湖北省农田环境监测工程技术研究中心开放基金(201603)
中文摘要:针对土壤重金属快速检测需求,基于模型集群分析方法进行特征波长变量选择,提出了利用X射线荧光光谱技术检测农田土壤中重金属含量的方法。采集91个配制土壤样品的X射线荧光光谱值,用于构建土壤重金属检测模型。通过多特征串联方法提取特征波长变量,首先采用区间组合优化算法(ICO)粗选波长,然后采用竞争适应性重加权采样法(CARS)剔除区间波长中的无关变量,最后采用连续投影算法(SPA)进行波长精简。通过多特征串联ICO-CARS-SPA算法对X射线荧光光谱进行特征变量选择,得到5组(26、25、29、39、33)特征波长点,据此建立Cu、Zn、As、Pb、Cr 5种土壤重金属含量偏最小二乘(PLS)检测模型,并与其他传统特征波长选择方法进行了对比。结果表明,ICO-CARS-SPA算法所选变量结合偏最小二乘(PLS)的建模效果最优,Cu、Zn、As、Pb、Cr的验证集决定系数分别为0.993 3、0.992 6、0.995 6、0.993 2和0.988 6,均方根误差分别为6.938 5、23.698 4、3.632 6、8.510 6和14764 5,验证集平均相对偏差分别为0.255 1、0.065 0、0.102 5、0.241 4、0.104 7。基于X射线荧光光谱结合多特征串联策略的ICO-CARS-SPA算法可剔除更多无效波长,提升有效信息贡献度,简化了检测模型复杂度,为土壤重金属含量预测模型选取合适的特征波段提取方法提供了理论支撑。
中文关键词:X射线荧光光谱(XRF)  土壤重金属  波长优选  模型集群分析
 
Prediction of Heavy Metal Contents in Soil Based on X-ray Fluorescence Spectroscopy with Multi-feature Series Strategy
Abstract:Aiming at the demand for rapid detection of heavy metals in soil,a selection for characteristic wavelength variables was performed based on model population analysis method,and a method of X ray fluorescence spectroscopy was proposed for the detection of contents of heavy metals in farmland soils.X ray spectrum values of 91 configured soil samples were collected,and used to establish a soil heavy detection model.The characteristic wavelength variables were extracted by the multi feature series method.Firstly,the interval combination optimization algorithm(ICO) was used for rough selection of the wavelength.Secondly,the competitive adaptive reweighted sampling(CARS) was adopted to remove those irrelevant variables in the interval wavelength.Finally,the successive projections algorithm(SPA) was performed for wavelength reduction.The ICO-CARS-SPA algorithm with multiple features was used to select the feature variables in the X ray fluorescence spectrum to obtain 5(26,25,29,39,33) characteristic wavelength points.Based on this,a partial least squares(PLS) detection model for the contents of five heavy metals,i.e.Cu,Zn,As,Pb,Cr in soil was developed,which was compared with other traditional characteristic wavelength selection methods.Results showed that the variables selected by the ICO-CARS-SPA algorithm were combined with partial least squares(PLS) to have the best modeling effect.The determination coefficients of Cu,Zn,As,Pb,Cr were 0.993 3,0.992 6,0.995 6,0.993 2,0.988 6,respectively.The root mean square errors of five metals were 6.938 5,23698 4,3.632 6,8.510 6 and 14.764 5,and the mean relative biases of the prediction sets were 0.255 1,0.065 0,0.102 5,0.241 4,0.104 7,respectively.The ICO-CARS-SPA algorithm based on X-ray fluorescence spectroscopy combined with multi-feature series strategy could eliminate invalid wavelengths,thus increasing contribution of effective information,simplifying detection model complexity,and providing a theoretical support for selecting an appropriate feature band extraction method for the prediction model of soil heavy metal content.
Key Words:X-ray fluorescence(XRF)  soil heavy metals  wavelength optimization  model population analysis
引用本文:任顺,张雄,任东,杨信廷,张力.基于X射线荧光光谱与多特征串联策略的土壤重金属含量预测[J].分析测试学报,2020,39(7):829-837.
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