Espectroscopia do solo no Vis-IR: potencial predictivo e desenvolvimento de uma interface gráfica de usuário em R

Abstract

This thesis presents a study of Visible Near-infrared spectroscopy technique applied to predict soil properties. The purpose was to develop quantitative soil information due to the demand of digital soil mapping, environmental monitoring, agricultural production and for increasing spatial information on soil. Soil spectroscopy emerge as an alternative to revolutionize soil monitoring, allowing rapid, low-cost, non-destructive samples sampling, environmental-friendly, reproducible, and repeatable analysis. To improve the efficiency of soil prediction using spectral data, several spectral preprocessing techniques and multivariate models were exploited. A graphical user interface (GUI) in R, named Alrad Spectra, was developed to perform preprocessing, multivariate modeling and prediction using spectral data. Hereby, the objectives were: The objectives were: i) to predict soil properties to improve soil information using spectral data, ii) to compare the performance of spectral preprocessing and multivariate calibration methods in the prediction of soil organic carbon, iii) to obtain reliable soil organic carbon prediction, and iv) to develop a graphical user interface that performs spectral preprocessing and prediction of the soil property using spectroscopic data. A total of 595 soil samples were collected in central region of Santa Catarina State, Brazil. Soil spectral reflectance was obtained using a FieldSpec 3 spectroradiometer with a spectral range of 350–2500 nm with 1 nm of spectral resolution. The outcomes of the thesis have demonstrated the great performance of predicting soil properties using Vis-NIR spectroscopy. Apparently, soil properties that are directly related to the chromophores such as organic carbon presented superior prediction statistics than particle size. Spectral preprocessing applied in the soil spectra contribute to the development of high-level prediction model. Comparing different spectral preprocessing techniques for soil organic carbon (SOC) prediction revealed that the scatter–corrective preprocessing techniques presented superior prediction results compared to spectral derivatives. In scatter–correction technique, continuum removal is the most suitable preprocessing to be used for SOC prediction. In the calibration modeling, excepting for random forest, all of methods presented robust prediction, with emphasis on the support vector machine method. The systematic methodology applied in this study can improve the reliability of SOC estimation by examining how techniques of spectral preprocessing and multivariate methods affect the prediction performance using spectral analysis. The development of easy-to-use graphical user interface may benefit a large number of users, who will take advantage of this useful chemometrics analysis. Alrad Spectra is the first GUI of its kind and the expectation is that this tool can expand the application of the spectroscopy technique.