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...

Near-infrared spectroscopy (NIRS), a rapid and efficient tool, was used to determine the total amount of nine ginsenosides in Panax ginseng. In the study, the regression models were established using multivariate regression methods with the results from conventional chemical analytical methods as reference values. The multivariate regression methods, partial least squares regression (PLSR) and ...

Online near-infrared spectroscopy was used as a process analysis technique in the synthesis of 2-chloropropionate for the first time. Then, the partial least squares regression (PLSR) quantitative model of the product solution concentration was established and optimized. Correlation coefficient (R (2)) of partial least squares regression (PLSR) calibration model was 0.9944, and the root mean sq...

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...

therefore, the objective of this study is to test the feasibility of uv-ir transmission spectroscopy for the non-destructive assessment of the freshness of an individual egg as an alternative for traditional inspection methods. to obtain a considerable variation in freshness, groups consisting of 10 eggs were stored (30 7 oc and 25% 4 rh) for 30 days. data obtained from non-destructive spectral...

Recently, there has been an increased attention in the literature on the use of ensemble methods in multivariate regression and classification. These methods have been shown to have interesting properties both for regression and classification. In particular, they can improve the accuracy of unstable predictors. Ensemble methods have so far, been little studied in situations that are common for...

Modeling and mapping of soil properties has been identified as key for effective land degradation management and mitigation. The ability to model and map soil properties at sufficient accuracy for a large agriculture area is demonstrated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Soil samples were collected in the El-Tina Plain, Sinai, Egypt, concurren...

Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop state during the vegetation period. Traditional ways of analytical BMP determination are based on fermentation trials and requi...

Aboveground biomass (AGB) is one of the strategic biophysical variables of interest in vegetation studies. The main objective of this study was to evaluate the Support Vector Machine (SVM) and Partial Least Squares Regression (PLSR) for estimating the AGB of grasslands from field spectrometer data and to find out which data pre-processing approach was the most suitable. The most accurate model ...

This paper investigates the use of Local Linear Embedded Regression (LLER) for the quantitative analysis of glucose from near infrared spectra. The performance of the LLER model is evaluated and compared with the regression techniques Principal Component Regression (PCR), Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) both with and without pre-processing. The predic...