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基于XRF和Vis-NIR光谱数据融合的土壤镉含量定量分析法
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作者单位
王清亚,李福生,江晓宇,邬书良,谢涛锋,黄温钢 1.东华理工大学核技术应用教育部工程研究中心2.东华理工大学核资源与环境国家重点实验室 
基金项目:核技术应用教育部工程研究中心开放基金(HJSJYB2018-5)
中文摘要:根据鄱阳湖南矶山区域土壤的X荧光光谱和可见近红外光谱特征,建立了3种数据融合(等权融合、累加融合、外积融合)的最小二乘向量机定量分析模型。结果表明,等权融合和外积融合模型精度和稳定性均优于单一光谱定量分析模型。其中外积融合模型性能最佳,其决定系数(R2)为0.85,校正均方根误差(RMSEC)为009,预测均方根误差(RMSEP)为006,相对分析误差(RPD)为2.41,满足实际土壤中Cd的检测需求。该方法准确可靠,可为我国土壤重金属分类分级方法研究提供参考。
中文关键词:X荧光光谱;可见近红外光谱;最小二乘支持向量机;镉含量;外积融合  土壤
 
Quantitative Analysis of Soil Cadmium Content Based on Fusion of XRF Data and Vis-NIR Data
Abstract:According to the characteristics of X-ray fluorescence spectrum and visible near infrared spectrum for soil in Nanji area of Poyang Lake,three quantitative analysis models for data fusion,including equal right fusion,co-addition fusion and outer product fusion based on least squares vector machine(LS-SVM) were established.Results showed that the models for equal right fusion and outer product fusion have better accuracy and stability than the single spectral quantitative analysis model has,in which the model for outer product fusion exhibits the best performance with a determination coefficient(R2) of 0.85,a root mean squared error(RMSEC) of 0.09,a root mean square error of prediction(RMSEP) of 0.06 and a relative percent deviation(RPD) of 2.41,satisfying the detection requirements.With the advantages of accuracy and reliability,the developed method could provide a reference for the study of soil heavy metal classification and grading method in China.
Key Words:X-ray fluorescence spectroscopy  visible and near infrared spectra  least square support vector machine  cadmium content  outer product fusion  soil
引用本文:王清亚,李福生,江晓宇,邬书良,谢涛锋,黄温钢.基于XRF和Vis-NIR光谱数据融合的土壤镉含量定量分析法[J].分析测试学报,2020,39(11):1327-1333.
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