Feasibility of using Vis-NIR spectroscopy and PXRF spectrometry to estimate regional soil cadmium concentration.

Conventionally, soil cadmium (Cd) measurements in the laboratory are expensive and time-consuming, involving complex processes of sample preparation and chemical analysis. This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance (Vis-NIR) spectroscopy and portable X-ray fluorescence spectrometry (PXRF) to estimate regional soil Cd concentration in a time- and cost-saving manner. The sensor data of Vis-NIR and PXRF, and Cd concentrations of 128 surface soils from Yunnan Province, China, were measured. Outer-product analysis (OPA) was used for synthesizing the sensor data and Granger-Ramanathan averaging (GRA) was applied to fuse the model results. Artificial neural network (ANN) models were built using Vis-NIR data, PXRF data, and OPA data, respectively. Results showed that: (1) ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation; (2) Fusion methods of both OPA and GRA had higher predictive power (R2) = 0.89, ratios of performance to interquartile range (RPIQ) = 4.14, and lower root mean squared error (RMSE) = 0.06, in ANN model based on OPA fusion; R2 = 0.88, RMSE = 0.06, and RPIQ = 3.53 in GRA model) than those based on either Vis-NIR data or PXRF data. In conclusion, there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately.

Data fusion for the measurement of potentially toxic elements in soil using portable spectrometers.

Soil contamination posed by potentially toxic elements is becoming more serious under continuously development of industrialization and the abuse of fertilizers and pesticides. The investigation of soil potentially toxic elements is therefore urgently needed to ensure human and other organisms' health. In this study, we investigated the feasibility of the separate and combined use of portable X-ray fluorescence (pXRF) and visible near-infrared reflectance (vis-NIR) sensors for measuring eight potentially toxic elements in soil. Low-level fusion was achieved by the direct combination of the pXRF and vis-NIR spectra; middle-level fusion was achieved by the combination of selected bands of the pXRF and vis-NIR spectra using the Boruta feature selection algorithm; and high-level fusion was conducted by outer-product analysis (OPA) and Granger-Ramanathan averaging (GRA). The estimation accuracy for the eight considered elements were in the following order: Zn > Cu > Ni > Cr > As > Cd > Pb > Hg. The measurement for Cu and Zn could be achieved by pXRF spectra alone with Lin's concordance correlation coefficient (LCCC) values of 0.96 and 0.98, and ratio of performance to interquartile distance (RPIQ) values of 2.36 and 2.69, respectively. The measurement of Ni had the highest model performance for high-level fusion GRA with LCCC of 0.89 and RPIQ of 3.42. The measurements of Cr using middle- and high-level fusion were similar, with LCCC of 0.86 and RPIQ of 2.97. The best estimation accuracy for As, Cd, and Pb were obtained by high-level fusion using OPA, with LCCC >0.72 and RPIQ >1.2. However, Hg measurement by these techniques failed, having an unacceptable performance of LCCC <0.20 and RPIQ <0.75. These results confirm the effectiveness of using portable spectrometers to determine the contents of several potentially toxic elements in soils.