Modeling of Chromium, Copper, Zinc, Arsenic and Lead Using Portable X-ray Fluorescence Spectrometer Based on Discrete Wavelet Transform

Abstract

A modeling method based on discrete wavelet transform (DWT) was introduced to analyze the concentration of chromium, copper, zinc, arsenic and lead in soil with a portable X-ray fluorescence (XRF) spectrometer. A total of 111 soil samples were collected and observed. Denoising and baseline correction were performed on each spectrum before modeling. The optimum conditions for pre-processing were denoising with Coiflet 3 on the 3rd level and baseline correction with Coiflet 3 on the 9th level. Calibration curves were established for the five heavy metals (HMs). The detection limits were compared before and after the application of DWT, the qualitative detection limits and the quantitative detection limits were calculated to be three and ten times as high as the standard deviation with silicon dioxide (blank), respectively. The results showed that the detection limits of the instrument using DWT were lower, and that they were below national soil standards; the determination coefficients (R2) based on DWT-processed spectra were higher, and ranged from 0.990 to 0.996, indicating a high degree of linearity between the contents of the HMs in soil and the XRF spectral characteristic peak intensity with the instrument measurement.

Quick Analysis of Organic Amendments via Portable X-ray Fluorescence Spectrometry

“…The technique has a wide range of quantifications, having detection limits poorer than those of large laboratory-based XRF instruments or of ICP techniques, but it is sufficient for environmental monitoring of a large number of elements that can be determined simultaneously [12,13]. Despite lower detection limits of the chemical methods, sometimes these become more imprecise and inaccurate than XRF spectrometry, mainly due to the fact that the latter requires virtually no sample pretreatment [14]. There are approved protocols for using this instrument in soils and sediments [15,16].…”

X-ray fluorescence spectroscopic analysis of trace elements in soil with an Adaboost back propagation neural network and multivariate-partial least squares regression

“…As an effective signal processing method, wavelet transform can not only peel off the characteristic peaks and get a background sum closer to the background [33], but also effectively reduce the measured spectral noise [34].…”

A new technique for baseline calibration of soil X-ray fluorescence spectra based on enhanced generative adversarial networks combined with transfer learning

“…Li et al used discrete wavelet transform (DWT) to subtract the background of the XRF spectrum to analyse the concentration of elements Cr, Cu, Zn, As and Pb in soil. 19 DWT is a multi-resolution analysis method that can extract spectral background. However, it cannot completely distinguish the overlapping parts of signals in wavelet space.…”