Assessing heavy metal concentrations in earth-cumulic-orthic-anthrosols soils using Vis-NIR spectroscopy transform coupled with chemometrics

Soil visible and near infrared (Vis-NIR) has become an applicable and interesting technique to predict soil properties because it is a fast, cost-effective, and non-destruction technique. This study presents an application of diffuse reflectance spectroscopy (DRS) and chemometric techniques for evaluating concentrations of heavy metals in earth-cumulic-orthic-anthrosols soils. 44 soil samples of 0-30 cm were collected from three representative agriculture areas (Fufeng, Yangling, and Wugong transacts with 16, 10, and 18 samples, respectively) and analyzed for Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb by Vis-NIR spectroscopy (350-2500 nm). Average levels of Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb were 17.95, 274, 12.77, 7.29, 15.81, 7.51, 0.40, 12.58, and 21.05 mg kg-1, respectively. Twenty-four preprocessing methods were extracted sensitive bands. Partial least squares regression (PLSR) used to obtain effective bands and predict soil heavy metals concentrations. The accuracy of the predictive models were assessed in terms of coefficient of determination (R-2), the root mean squared error (RMSE), standard error (SE) and the ratio of performance to deviation (RPD). The results revealed that excellent predictions for Hg(Rv(2) = 0.99, RPD = 8.59. RMSEP = 0.12, SEP = 0.13), Cr (Rv(2) = 0.97, RPD = 5.96, RMSEP = 0.10, SEP = 0.10), Ni (Rv(2) = 0.93, RPD = 3.74, RMSEP = 0.13. SEP = 0.13), Pb (Rv(2) = 0.97, RPD = 5.57. RMSEP = 0.10, SEP = 0.01), and Cu (Rv(2) = 0.92. RPD = 3.38, RMSEP = 0.08, SEP = 0.08). Models for As (Rv(2) = 0.87, RPD = 2.58), Mn (Rv(2) = 0.80, RPD = 2.09), and Cd (RPD = 2.77) had Rv(2) < 0.9 and RPD<3.0, not excellent predictions. For the element of Zn, although Rv2 = 0.91, RPD = 3.13, the offset had too much deviation, and it cannot be considered an excellent model. Therefore, a combination of spectroscopic and chemometric techniques can be applied as a practical, rapid, low-cost and quantitative approach for evaluating soil physical and chemical properties in Shaanxi, China. (C) 2019 Elsevier B.V. All lights reserved.