Use of Near-infrared Spectroscopy to Distinguish Carbon and Nitrogen Originating from Char and Forest-floor Material in Soils: Usefulness of a Genetic Algorithm

Abstract

Abstract Several algorithms exist for the calibration procedures of near-infrared spectra in soil-scientific studies, but the potential of a genetic algorithm (GA) for spectral feature selection and interpretation has not yet been sufficiently explored. Objectives were (1) to test the usefulness of near-infrared spectroscopy (NIRS) for a prediction of C and N from char and forest-floor Oa material in soils using either a partial least squares (PLS) method or a GA-PLS approach and (2) to discuss the mechanisms of GA feature selection for the examined constituents. Calibration and validation were carried out for measured reflectance spectra in the visible and near-IR region (400–2500 nm) on an existing set of 432 artificial mixtures of C-free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals), and forest-floor Oa material. For all constituents (total C and N, C and N from all coals and from the Oa material, C derived from mixed coal, charcoal, lignite, and anthracite), the GA-PLS approach was superior over the full-spectrum PLS method. The RPD values (ratio of standard deviation of the laboratory results to standard error of prediction) ranged from 2.4 to 5.1 in the validation and indicated a better category of prediction for three constituents: approximate quantitative'' instead of a distinction between high and low’’ for C derived from mixed coal and good'' instead of approximate quantitative’’ for C and N derived from all coals. Overall, this study indicates that the approach using GA may have a greater potential than the PLS method in NIRS.