Chemometrics Research Of Spectroscopy Analysis And Its Application In Near-infrared Analysis Of Soil

Near-infrared (NIR) spectroscopy is a direct quantitative analysis technique. It can analyze samples in real time without chemical reactions, which makes it a great advantage on the application, but also there are great difficulties in methods to overcome. As for complex systems, the near-infrared spectrum includes a variety of noises, chemometrics methods must be used to eliminate these noises, of which there are many challenging mathematical problems, such as the calibration samples analysis, spectral preprocessing modes, spectral waveband selection optimization and so on.Soil is the most important sustainable development of agriculture component. The nutritional content of the soil (organic matter, total nitrogen) is an important indicator to measure soil fertility. Simple and rapid reagent-free determination method for soil content is the critical need for modern agricultural technologies. As soil is a complex system with multi-component, the study of high precision models of near-infrared spectroscopy analysis for soil is much significant, taking this as the objective, we research a number of core chemometrics methods in this paper.Firstly, we study the spectral preprocessing methods based on Savitzky-Golay (SG) smoothing; secondly, research the optimization methods for continuous spectral waveband on moving window partial least squares (MWPLS), explore the optimization method for discrete spectral wavelength combination founded on equidistant combination moving window multiple linear regression (ECMWMLR), and then further optimize models joined with SG smoothing; thirdly, for reducing model complexity and providing the reference of designing special instruments, we propose a spectral dimension reduction method based on the optimal combinational wavelength, and experimentally confirmed its effectiveness. In addition, to get stable and reliable results, all optimal models in this paper are obtained by multiple divisions of calibration set and prediction set, and a rational dividing method is proposed.We build up a computer algorithm platform, for the integration of chemometrics methods for NIR spectroscopy analysis, and respectively establish NIR analysis models for soil organic matter and total nitrogen, and further examine the models. For organic matter, its optimal MWPLS model shows, the waveband is 1692-1880 nm, PLS factor is 14, root mean square error of prediction (RMSEP) and correlation coefficient of prediction (RP) are 0.275 (%) and 0.870, respectively; while in its optimal ECMWMLR model, the beginning wavelength is 1786 nm, the number of adopted wavelengths is 9, the gap of adopted wavelengths is 20, RMSEP and RP are 0.265 (%) and 0.871, respectively. For nitrogen, its optimal MWPLS model indicates, the waveband is 1600-2198 nm, PLS factor is 11, RMSEP and RP are 0.0145 (%) and 0.886, respectively; while in its optimal ECMWMLR model, the beginning wavelength is 1716 nm, the number of adopted wavelengths is 9, the gap of adopted wavelengths is 31, RMSEP and RP are 0.0141 (%) and 0.891, respectively. Results prove that the prediction effects of these optimal models are obviously better than that of the traditional analysis models, such as PLS and SG-PLS models on the whole spectral collecting region, and these optimal model is more simple and stable, providing high precision practical model for NIR spectroscopy applying to soil analysis, the spectral wavebands and the spectral wavelength combinations provide important references for designing specific NIR instrument. The methodological framework and the computer algorithm platform here can also be used for the NIR spectroscopy analysis of other complex systems.