Soil organic carbon stock plays a key role in the global carbon cycle and the precision agriculture. Visible and near-infrared reflectance spectroscopy (VNIRS) can directly reflect the internal physical construction and chemical substances of soil. The partial least squares regression (PLSR) is a classical and highly commonly used model in constructing soil spectral models and predicting soil p...

In this paper, a novel hybrid Radial Basis Function Neural Network (RBF–NN) ensemble model is proposed for rainfall forecasting based on Kernel Partial Least Squares Regression (K–PLSR). In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input to t...

Quantitative analysis of CaO in limestone mining is mandatory, not only for ore exploration, but also for grade control. A partial least squares regression (PLSR) CaO estimation technique was developed for limestone mining. The proposed near-infrared spectroscopy (NIR)-based method uses reflectance spectra of the rock sample surface in the visible to short-wave infrared wavelength regions (350–...

In multivariate calibration by for example ordinary least squares (OLS) multiple regression or by partial least squares regression (PLSR) the predictor ŷ(x) is perfect for the calibration sample itself, in the sense that the regression of observed y on predicted ŷ(x) is y = ŷ(x). Plots of y against ŷ(x) are much used to illustrate how good the calibration is and how well prediction works. Usual...

Soil samples were collected from five provinces over China, including Beijing, Xinjiang, Heilongjiang, Yunnan, and Jiangsu. Heavy metal Cr, Zn, Pb and As in soils were analyzed by a portable X-ray fluorescence spectrometry (XRF). For predicating metal concentration in soils, a partial least square regression model (PLSR) was established. After cross-calibration, the correlation coefficients for...

Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate rapid monitoring of soil salinity its spatial variability is great significance for the amelioration saline soils. In this study, 191 samples were collected from cotton fields southern Xinjiang, China, to obtain spectral reflectance electrical conductivity (EC) indoors. Then, mu...

Multivariate statistics are commonly used to identify the factors that control the dynamics of runoff or sediment yields during hydrological processes. However, one issue with the use of conventional statistical methods to address relationships between variables and runoff or sediment yield is multicollinearity. The main objectives of this study were to apply a method for effectively identifyin...

A new method for quantifying adrenaline and noradrenaline concentrations from mixtures of catecholamine standards is described. The method derives selectivity from the different rates, at which the fluorescing 3,5,6-trihydroxyindole derivatives (lutines) of the catecholamines are formed and degraded for adrenaline and noradrenaline. The standards used had the concentration ranges 50–1200 nmol/l...

The characterization of plant nutrients is important to understand the process of plant growth in natural ecosystems. This study attempted to evaluate the performances of univariate linear regression with various vegetation indices (VIs) and multivariate regression methods in estimating grass nutrients (i.e., nitrogen (N) and phosphorus (P)) with canopy hyperspectral reflectance. Synthetically ...

Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure based drug discovery. However, the relationship between binding free energies and biological activities (pIC50) of drug candidates is still an unsolved issue that limits the efficiency and speed of drug development processes. In this study, the relationship between them is...