Non-destructive elemental analysis of herbal teas from South Africa

Utilizing X-ray fluorescence (XRF) method to evaluate the content of metal elements in soil and their effects on the total phenolic and flavonoid contents of some medicinal plants

Soil factors, especially metal elements in the soil, play a significant role in forming and accumulating secondary metabolites, which determine the medicinal properties of medicinal herbs. In this study, the concentrations of some metal elements (K, Mn, Fe, Cu, Zn, and Cr) in Cam Mountain and Tinh Bien Town, An Giang Province, Vietnam, were determined using the XRF method. We simultaneously determined the total phenolic and flavonoid content of some medicinal herbs collected from the collected soil sample areas, thereby assessing the influence of these elements on the formation of secondary metabolites in medicinal plants. The results showed that K, Mn, and Cr were mainly concentrated in the topsoil and transition layers; Fe and Cu elements tended to concentrate in the transition layer and the subsoil when surveying the soil profile. K, Mn, Cu, and Cr concentrations were more focused in Tinh Bien area, while Fe and Zn had higher concentrations at Cam Mountain. Additionally, results from evaluating the relationship between the content of the elements in the soil and the content of two active compounds also showed the correlation regression model between Zn and flavonoid expression by level 4 at the 5% significance level. Thus, the nonlinear model is suitable for evaluating the relationship between the content of metal elements in the soil and the active compound in medicinal plants.

Quantitative analysis of heavy metals in soil by X-ray fluorescence with PCA-ANOVA and support vector regression

Heavy metal concentration is an important index for evaluating soil pollution. It is of great significance to measure the trace element content accurately for green agriculture development. In order to detect the trace element content accurately, a new prediction framework including pre-processing, signal extraction, feature selection and decision-making was proposed. The energy dispersive X-ray fluorescence (ED-XRF) spectra of 57 national standard soil samples were investigated based on the proposed methods. Firstly, an innovative background deduction method called iterative adaptive window empirical wavelet transform (IAWEWT) was introduced to extract effective counts of characteristic peaks, and the proposed approach was validated by the coefficient of determination (R²) of the instrumental calibration curve compared with two other conventional methods. Secondly, principal component analysis (PCA) was combined with the analysis of variance (ANOVA) for variable selection optimization of the ED-XRF spectrum. After PCA feature extraction and ANOVA variable selection treatment, the optimum number of principal components for V, Cr, Cu, Zn, Mo, Cd and Pb were determined to be 7, 15, 4, 4, 4, 5 and 12 respectively. Furthermore, the support vector regression (SVR) model was adopted for heavy metal estimation. The evaluation indices included R² and root mean square error (RMSE). It was demonstrated that the predictive capabilities of seven heavy metal elements were improved substantially for elemental analysis by the proposed PCA-ANOVA-SVR model, with excellent results for V, Cr, Cu, Zn, Mo, Cd and Pb estimates, and the R² values were 0.993, 0.996, 0.999, 0.999, 0.997, 0.998 and 0.998 respectively. Therefore, the new framework proposed in this paper can effectively eliminate redundant features and determine the concentration of trace elements in soil. It provides an effective alternative for the quantitative analysis of X-ray fluorescence spectrometry.