光谱学与光谱分析, 2018, 38 (12): 3934, 网络出版: 2018-12-16   

一种面向土壤重金属含量检测的X射线荧光光谱预处理方法研究

An X-Ray Fluorescence Spectroscopy Pretreatment Method for Detection of Heavy Metal Content in Soil
任东 1,2沈俊 1,2任顺 1,2王纪华 1,2,3陆安祥 3
作者单位
1 三峡大学计算机与信息学院, 湖北 宜昌 443002
2 湖北省农田环境监测工程技术研究中心, 三峡大学, 湖北 宜昌 443002
3 北京农业质量标准与检测技术研究中心, 北京 100097
摘要
土壤重金属的污染影响着农作物的产量和质量。 传统的土壤重金属检测方法步骤繁琐、 检测费用高且速度慢。 利用X射线荧光光谱(XRF)分析技术检测土壤中重金属含量, 具有处理简单、 现场、 快速、 无损等优点。 由于土壤背景复杂, 包含大量噪声和无关信息, 建立XRF校正模型前, 对光谱的预处理能有效的去除不相干信息, 保留有用信息, 对XRF预测模型的精度有重要影响。 主要研究光谱预处理方法对重金属含量预测模型精度的影响。 首先, 采用向前间隔偏最小二乘(FiPLS)作为校正模型, 对比了无预处理、 去趋势处理(DT)、 标准正态变量变换(SNV)、 多元散射校正(MSC)、 小波去噪(WT)、 SNV+DT、 卷积平滑(SG)+一阶导数、 卷积平滑(SG)+二阶导数等7种不同预处理条件下的土壤重金属模型的检测精度。 初步结果表明, 多元散射校正预处理方法效果较好, 与原始光谱相比, 相关系数r从原始的0.988提高到0.990, 预测均方根误差RMSEP、 相对误差平均从原来的20.809和0.166分别降低到19.051和0.121。 其次, 在多元散射校正预处理方法的基础上, 针对多元散射校正方法以线性表达式描述非线性关系的局限性, 提出了局部加权线性回归多元散射校正(LWLRMSC)和偏最小二乘多元散射校正(PLSMSC), 并比较了它们的建模效果。 LWLRMSC是基于加权思想, 在预测一个点的值时, 选择适当的核函数和权重分配策略进行预测点的线性回归, 来解决简单线性回归的欠拟合状况; PLSMSC是基于PLS建模思想, 考虑了自变量和因变量的最大相关性, 来减少拟合误差及失真问题。 结果表明, PLSMSC具有最佳的预处理效果, 五种重金属Cu, Zn, As, Pb, Cr预测值和实际值的R分别为0.989, 0.973, 0.991, 0.989和0.986, RMSEP分别为8.805, 58.360, 7.671, 12.549和20.851, 相比于传统的MSC方法不仅在精度方面有大幅度的提升, 且具有更好的泛化性能, 能消除光谱噪声, 提升有效信息贡献度, 为土壤重金属含量预测模型选取合适的预处理方法提供了理论支撑。
Abstract
Heavy metal pollution in the soil affects the yield and quality of crops. The traditional detection method has complicated procedures, high detection costs, and slow detection speed. The X-ray fluorescence (XRF) analysis technology to detect heavy metal content in soil has the advantages of being simple in processing, on-site, rapid and non-destructive. Due to the complex soil background including much noise and irrelevant information, before the establishment of the XRF correction model, the pre-processing of the spectrum can effectively remove irrelevant information and maintain useful information, which has an important influence on the accuracy of the XRF prediction model.This article focuses on the effects of spectral pre-processing method on the accuracy of heavy metal content prediction model. Firstly, forward interval partial least squares (FiPLS) was taken as a correction model to compare the detection accuracy of the heavy metal model in eight different conditions, namely non-pre-processing, detrending processing (DT), standard normal variable transformation (SNV), multiple scatter correction (MSC), wavelet denoising (WT), SNV+DT, convolution smoothing (SG) + first derivative and convolution smoothing (SG) + second derivative. The preliminary results showed that the multiple scatter correction pre-treatment method had desirable effects. Compared with the original spectrum, the determination coefficient R rised from the original 0.988 to 0.990, and the prediction of root mean square error (RMSEP) and the relative error respectively declined from the original 20.809 and 0.166 to 19.051 and 0.121. Secondly, on the basis of the multi-dimensional scattering correction pre-processing method, the localized weighted linear regression multiple scatter correction (LWLRMSC) and partial least squares multivariate scatter correction (PLSMSC) were proposed in terms of the restriction of describing non-linear relationships with linear representations, and the modeling effects of LWLRMSC and PLSMSC were compared. LWLRMSC was based on the weighted idea. In the prediction of the value of a point, the proper kernel function and weight distribution strategy were selected to perform linear regression of the prediction point, and the under-fitting condition of the simple linear regression was resolved. PLSMSC, based on the PLS modeling idea and taking into account the maximum correlation between the independent variable and the dependent variable, reduced the fitting error and distortion. The results showed that PLSMSC has the best pre-treatment effects. The R values of the predicted and actual values of the five heavy metals (Cu, Zn, As, Pb and Cr) were 0.989, 0.973, 0.991, 0.989 and 0.986, with the RMSEP respectively being 8.805, 58.360, 7.671, 12.549 and 20.851. Compared with the traditional MSC method, PLSMSC not only has a significant improvement in accuracy but also has better generalization performance. It can eliminate spectral noise and improve the contribution of effective information, thus providing theoretical support for the soil heavy metal content model to select the suitable pre-treatment method.

任东, 沈俊, 任顺, 王纪华, 陆安祥. 一种面向土壤重金属含量检测的X射线荧光光谱预处理方法研究[J]. 光谱学与光谱分析, 2018, 38(12): 3934. REN Dong, SHEN Jun, REN Shun, WANG Ji-hua, LU An-xiang. An X-Ray Fluorescence Spectroscopy Pretreatment Method for Detection of Heavy Metal Content in Soil[J]. Spectroscopy and Spectral Analysis, 2018, 38(12): 3934.

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