Prediction of organic carbon and calcium carbonates in agricultural soils with Vis-NIR spectroscopy

Abstract

The objectives of this study were to (i) evaluate the ability of Vis-NIR spectroscopy to predict soil organic carbon (SOC) and CaCO 3 content in the heterogeneous agricultural soils from Dalmatia, Croatia and (ii) to compare the performance of two multivariate calibration techniques: partial least square regression (PLSR) and support vector machine regression (SVMR). The reflectance spectra of a total of 250 top-soils (0-25 cm) samples were collected in the laboratory using a portable Terra Spec 4 HiRes Mineral Spectrometer with a wavelength range 350-2500 nm. The coefficient of determination (R 2), the residual prediction deviation (RPD) and the root mean square error (RMSE) were used for the model evaluation. The CaCO 3 prediction derived by PLSR and SVMR with R 2 (0.86 and 0.88) and RPD (2.67 and 2.82), respectively are considered good prediction models. The SOC prediction with SVMR (R 2 0.84 and RPD 2.43) indicates good prediction and approximate quantitative prediction with PLSR with R 2 of 0.78 and RPD of 1.94. Our results showed that (i) CaCO 3 and SOC estimations were obtained with acceptable accuracy using Vis-NIR spectroscopy, (ii) the SVMR method produced more accurate estimations of selected soil properties compared to PLSR, and (iii) Vis-NIR spectroscopy, in combination with SWMR can be recommended as a rapid and inexpensive method for screening of the CaCO 3 and SOC content.

Evaluation of the physicochemical content and solid-state fermentation stage of Zhenjiang aromatic vinegar using near-infrared spectroscopy

“…Near-infrared (NIR) spectroscopy, as a nondestructive analysis method, has been widely used in various detection fields such as the petrochemical industry [12], medicine [13,14], and agriculture [15][16][17]. Jiang et al [18] built a model using stable synergy interval partial least squares (SiPLS) and NIR to detect the moisture content and pH value during SSF of wheat straw.…”