Quantitative analysis of heavy metals in soil by X-ray fluorescence with improved variable selection strategy and bayesian optimized support vector regression

As a sensitive analytical technique, x-ray fluorescence (XRF) has received increasing attention in elemental analysis applications. In this paper, XRF technique coupled with improved variable selection strategy and bayesian optimized support vector regression was proposed for predicting heavy metals in the soil. First of all, an iterative variational mode decomposition (VMD) framework was provided to tackle continuous background interference in the XRF spectra of soil. The corrected spectra were then utilized for quantitative analysis of heavy metals. Aiming to implement accurate heavy metal prediction, we resorted to support vector regression (SVR) to establish the non-linear model. Combined with Bayesian optimization algorithm, the bayesian optimized SVR (BO-SVR) with optimal hyperparameters was obtained. However, severe collinearity between input variables can affect the accuracy and stability of the model. Towards this, the research utilized sensitivity analysis as the variable selection approach prior to BO-SVR prediction of XRF data sets. The experimental results revealed that BO-SVR assisted with VMD and sensitivity analysis yielded the best model outcome. Moreover, considerable contributions were observed by employing sensitivity analysis. It showed R2 ������ of 0.9472 (for Cu), 0.7534 (for As), 0.9374 (for Zn), and 0.9283 (for Pb), and RMSEP of 58.0472 (for Cu), 16.5271 (for As), 63.2069 (for Zn) and 64.0662 (for Pb) without using variable selection technique. Resorting to sensitivity analysis, the R2 ������ of Cu, As, Zn and Pb achieved 0.9918, 0.9526, 0.9611 and 0.9541, respectively and the RMSEP of Cu, As, Zn and Pb achieved 22.8803, 11.6868, 38.8753 and 32.9387, respectively. The results of this study clearly illustrated that the proposed model is an effective method for the analysis of heavy metals in soil.