Quantitative analysis of heavy metals in soil by X-ray fluorescence: Fusion, intelligentization and Nonstandard-sample calculation

¹ College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing, 100029, China
² Beijing Key Laboratory of Environmentally Harmful Chemicals Analysis, 100029, China

^* Corresponding author: yinliang@mail.buct.edu.cn

Abstract

Qualitative and quantitative analysis of heavy metal elements in soil by X-ray fluorescence (XRF) has received widespread attention and research from scholars as an important method for assessing environmental pollution. As a detection sample for multi-component systems, the study of matrix correction has always been the key to XRF data analysis for geological samples. In this study, we reviewed the calculation and analysis methods of XRF data used for geological samples since the Sherman equation was proposed, and divided the development of XRF data processing for soil samples into three stages based on the changes in the matrix correction methods used. By reviewing the processing ideas from past research, this paper summarizes the process of quantitative analysis of geological samples into seven stages and reviews the commonly used methods for each stage. Due to limitations in instrument and standard sample costs, as well as methodological constraints, geological samples currently face three challenges: a shortage of standard samples, insufficient generalization ability of established models, and large measurement errors in low-content element determination. With the further cross-penetration of multiple fields and disciplines and the summary of past research trends, we propose three research trends that may break through these limitations: fusion, intelligentization, and nonstandard-sample calculation. We also discuss the technical solutions related to these three research trends. We extensively discussed the feasibility and advantages of using spectral co-use, knowledge engineering, and adversarial data augmentation techniques to address problems. Our review provides insights into the XRF spectral data processing methods and frameworks for evaluating geological samples, and provides technical solutions to address the current challenges faced by XRF analysis of geological samples.