Multi-scale convolutional attention GRU network combined with improved CARS strategy to determine key elements in ores by XRF.

Background: X-ray fluorescence (XRF) technology is a promising method for estimating the metal element content in ores, which helps in understanding ore composition and optimizing mining and processing strategies. However, due to the presence of a large number of redundant features in XRF spectra, traditional quantitative analysis models struggle to effectively capture the nonlinear relationship between element concentration and spectral information of XRF, making it more difficult to accurately predict metal element concentrations. Thus, analyzing ore element concentrations by XRF remains a significant challenge.

An integrated BLS-Net optimized with dual PCA and improved PSO-CARS variable selection strategy to determine heavy metals in soil by XRF.

Combining machine learning with X-ray fluorescence (XRF) spectroscopy is a promising solution for quantitatively analyzing heavy metal elements in soil. However, the implied linear and nonlinear interferences between spectral intensities and elemental concentrations are difficult to quantify by a single model, thus degrading the prediction performance for low-concentration elements. This paper presents a novel combined spectral variable selection and fusion modeling framework for quantitative analysis of heavy metal elements in soil XRF, which consists of the proposed Particle Swarm Optimization-based Competitive Adaptive Re-weighted Sampling (PSO-CARS) by adaptive decay strategy and Dual Principal Component Analysis-based broad learning system (BLS-Net).

An integrated CBLA-Net with fractional discrete wavelet transform and frequency-based CARS to predict heavy metal elements by XRF.

Background: X-ray fluorescence (XRF) emerges as a promising technique for estimating heavy metal elements. However, XRF spectra typically contain a significant amount of environmental information and signal noise, and the relationship between spectral intensity and element concentration is difficult to quantify using a single model, thereby reducing the predictive performance for low concentration elements.

Results: This paper proposed a comprehensive framework for predicting elemental concentrations, encompassing preprocessing, variable selection, decision-making, to enable fast, non-destructive, and accurate estimation of element concentrations in soil.