Improvement of Partial Least Squares Modelling for Determination of Soil Nitrogen by Fourier Transform Near-Infrared Spectrometry

Abstract

The concentrations of nitrogen in soil were analyzed by the Fourier transform near-infrared (FT-NIR) spectrometry. Using partial least squares (PLS) regression, wavelength selection is a vital task for improving the modeling ability because the predictive results partially depend on the signal-to-noise ratio of the modeling wavelengths, The discrete single-and-favorite combination linear regression (DSFCLR) method was proposed for selecting the informative wavelength combination. And the discrete combination partial least squares (DCPLS) models were established on the informative wavelengths. Compared to moving window partial least squares modeling and full-range partial least squares modeling, DCPLS modeling observe improved predictive results and appreciate validation effects. Considering the optimal selected discrete combination contained only 32 wavelengths, the computational complexity was substantially reduced. discrete combination partial least squares models with appropriate discrete wavelengths corresponded to the characteristic absorption of nitrogen can effectively overcome collinearity interruption for the linear regressions. Therefore, DSFCLR method has physical and chemical significance while retaining the simplicity of linear regression. discrete single-and-favorite combination linear regression combined with DCPLS modeling is expected to be a new chemometric technique in spectroscopic analysis for selecting discrete wavelength combination.

LSSVM Combined with SPA Applied to Near-Infrared Quantitative Determination of the Octane in Fuel Petrol Samples

“…The prospective precise of calibration model will be difficult to improve if the full-range NIR spectrum is used. Wavelength selection is quite necessary for model optimization [11] [12]. Successive projection algorithm (SPA) is an effective method to search wavelengths (or wavelength combinations) which are most informative and with least colinearity [13].…”

Investigation of sample partitioning in quantitative near-infrared analysis of soil organic carbon based on parametric LS-SVR modeling

“…[15][16][17] It has been demonstrated that SOC can be measured in the eld with enhanced measurement stability at the expense of a slight decrease in accuracy compared to laboratory experiments. [18][19][20] Once the spectra have been calibrated for SOC, the chemometric methods can provide a rapid and inexpensive estimation of SOC in the eld.…”