Study on Soil Carbon Estimation by On-the-Go Near-Infrared Spectra and Partial Least Squares Regression with Variable Selection

The present paper tried to evaluate the effectiveness and improvement of variable selection before modeling with partial least squares regression (PLSR). Based on the independent test dataset, and compared with the PLSR model derived from all spectral variables, the prediction accuracy by modeling after variable selection has been improved. Thus, the results showed that variable selection was beneficial and necessary for soil carbon modeling by on-the-go NIRS. UVE (uninformative variable elimination) and UVE-SPA (successive projection algorithm) could perform effective variable selection and created promising models, and SPA and GA-PLS (genetic algorithm PLS) failed to make appropriate models. For synergy interval PLS (siPLS), change in interval number and number of interval for modeling could affect the prediction accuracy obviously. Promising models could be made by selecting appropriate interval number and number of interval for modeling, and siPLS could achieve similar prediction accuracy to UVE or UVE-SPA, and the shortcoming was that siPLS required a lot of computing time to find optimal combination of intervals for modeling.