摘要
针对土壤背景估计算法参数确定后适应性较差的问题,提出一种基于径向基函数(RBF)神经网络模型的土壤背景估计算法,以有效提升针对土壤的能量色散型X射线荧光检测的背景扣除效果及元素定量精度.首先分析了常用的土壤背景估计模型,针对连续剥峰法和小波变换对背景估计的扣除效果和问题提出基于改进RBF神经网络的算法模型,然后从理论上证明该算法模型的有效性,并将该模型应用于实际的土壤能量色散型X射线荧光检测系统中,对国家标准土壤样品进行检测,对Cr,Zn和As等重金属元素的定量探测进行深入分析.实验结果表明,基于该土壤背景估计算法能更好地进行元素能量特征值提取,降低背景对元素特征峰和质量分数的影响,进而有效提升土壤元素的定量精度.
Aiming at the problem of poor adaptability of soil background estimation algorithm after parameter determination,we proposed a soil background estimation algorithm based on radial basis function(RBF)neural network model,which could effectively improve the background deduction effect and element quantitative accuracy of energy-dispersive X-ray fluorescence detection for soil.We first analyzed the commonly used background estimation models,and proposed an algorithm model based on improved RBF neural network for the deduction effect and problem of continuous peak-stripping method and wavelet transform on background estimation,then we theoretically proved the validity of the algorithm model and applied it to the actual soil energy-dispersive X-ray fluorescence detection system to detect the national standard soil samples,and the quantitative detection of heavy metal elements such as Cr,Zn and As was analyzed in depth.The experimental results show that the soil background estimation algorithmr can better extract the energy eigenvalues of elements,reduce the influence of background on the characteristic peaks and mass fractions of elements,and effectively improve the quantitative accuracy of soil elements.
作者
江晟
叶新
刘妍秀
李开太
赵鹏
李野
JIANG Sheng;YE Xin;LIU Yanxiu;LI Kaitai;ZHAO Peng;LI Ye(School of Physics,Changchun University of Science and Technology,Changchun 130022,China)
出处
《吉林大学学报（理学版）》
CAS
北大核心
2023年第3期577-582,共6页
Journal of Jilin University:Science Edition
基金
吉林省重点科技研发项目(批准号:20180201112GX)。
关键词
能量色散型X射线荧光检测
土壤元素分析
径向基函数神经网络模型
背景估计
energy dispersive X-ray fluorescence detection
soil element analysis
radial basis function neural network model
background estimation
