RESUMO
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.
RESUMO
Near infrared spectroscopy (NIRS) is a rapid, pioximal-sensed method that has proven useful in quantifying soil constituents mainly in laboratory. However, very little is known about how NIRS performs in a field setting by newly developed on-the-go NIRS measurements. The objective of the present study was to evaluate the relationship between on-the-go field NIRS measurements and soil texture in a glacial till soil. It was found that NIRS band combination based on difference, normalized difference and ratio could apparently improve the coefficient of relationship between NIRS and soil texture, and this might be a new and effective analytical procedure for field NIRS measurements.
