Identification study of soil types based on feature factors of XRF spectrum combining with machine learning

Abstract

Soil type significantly influences the detection accuracy of heavy metals using X-ray fluorescence (XRF) technology. Rapid and accurate soil type identification is crucial for selecting appropriate XRF quantitative analysis methods for soil heavy metals, thereby enhancing analysis accuracy. This study utilized 26 soil samples from 10 distinct soil types, extracting 13 feature factors for soil type identification by analyzing XRF spectral variability. These factors were then integrated with three machine learning methods: Random Forest (RF), Support Vector Machine (SVM), and Backpropagation Neural Network (BPNN). The effectiveness of these methods in soil type identification was compared, highlighting the importance of XRF spectral feature factor extraction. The results demonstrate that identification based on feature factor extraction from XRF spectral variability markedly improves identification accuracy, stability and speed compared to full-spectrum XRF analysis. When identifying soil types by the gross area of spectral peaks of XRF feature factors, the accuracies of three machine learning methods—RF, SVM, and BPNN—were 99.62%, 99.04%, and 98.85%, respectively. Random Forest achieved the highest accuracy (99.62%) and fastest operation speed (0.179 s). Therefore, by extracting the differential features of XRF spectra and combining them with machine learning methods, it is possible to quickly and accurately recognize and judge soil types. This study demonstrates the successful and accurate identification of soil types using machine learning combined with XRF spectroscopy.

It establishes an important methodological foundation for the future development of fast and accurate field testing equipment for soil heavy metals using XRF technology.