ABSTRACT
To explore the potential of thermal infrared hyperspecra for retrieving sand content in soil, the sandy soil was measured using a 102F Fourier Transform Infrared Spectroradiometer (FTIR), and the characteristics of sandy soil's emissivity spectra were discussed based on correlation analysis and principal component analysis. Moreover, the sand contents were predicted using two modeling methods: Partial least squares regression (PLSR) and principal component regression (PCR). The results show that the Reststrahlen feature (RF) of SiO2 is obvious in the emissivity spectra of sandy soil with two large asymmetrical absorption troughs near 8.13 and 9.17 microm and two small troughs in the region of 12-13 microm. Soil emissivity becomes lower when sand content increases, this trend is more evident especially in the regions of 8-9.5 microm and 9.5-10.4 microm of which correlation coefficients are above 0.65 and 0.5 respectively, and these two regions can account for 84.07% of total emissivity variance. Predictive precision varies significantly when sand content is predicted by different modeling methods or spectral variables. The PLSR model can achieve the highest predictive precision by using first-order derivative spectra, and it's RMSE of modeling and prediction is 0.45 and 0.53 respectively, and the R2, 0.9907 and 0.9836, which means that the thermal hyperspectra has promising potential for retrieving sand content in soil.
ABSTRACT
In the present study, soil samples were scanned by NITON XLt920 field portable X-ray fluorescence (FPXRF) analyzer, and the relationship between the X-ray fluorescence spectra and the concentration of Pb in soil was studied. For predicating the Pb concentration in soil, a partial least square regression model (PLS)was established with 6 optimal factors and two closely relevant electron volt ranges: 10.40-10.70 keV and 12.41-12.80 keV. After cross-calibration, the correlation coefficient of value predicted by PLS model against that measured by ICP was 0.9666, and the root mean square error of prediction (RMSEP) was 0.8732. Meanwhile, the univariate linear regression and multivariate linear regression models were also built with the correlation coefficient of 0.6805 and 0.7302, respectively. Obviously, the PLS method was better than the other two methods for predication. Comparing to the conventional approach of atomic absorption spectroscopy (AAS), FPXRF has the advantages of rapidness, non-destruction and relatively low cost with the acceptable accuracy. It would be a powerful tool to decide which sample is needs for further analysis.
ABSTRACT
The existence of fake tea from non-origin seriously impacts on the credibility of the famous tea. A method was developed to identify tea from difference regions on the basis of the fact that the content of heavy metals in different origin tea is varied by using X-ray fluorescence technique and pattern recognition technique. Samples from different origins were grouped respectively, and their X-ray fluorescence spectra were acquired, and then the principal components of these spectral data were calculated, and the average of the principal components of each group was used as the center of each group. The Mahalanobis distance value between a sample and the center of a group were calculated, when the Mahalanobis distance value reached minimum, the sample was classed to current group, and in this way, a sample was identified. A Niton 792 portable X-ray spectrometer was used to class 120 tea samples from Anji, Jinhua, Hangzhou and Taizhou, in zhejiang province of China. It was found that the spectra between 3 and 13 KeV and the first 4 principal components give enough information for the identification of tea from different regions,and the rate of error was 4.2%.
