Regression models based on new local strategies for near infrared spectroscopic data
2016
Allegrini, F. | Fernández Pierna, J.A. | Fragoso, W.D. | Olivieri, A.C. | Baeten, V. | Dardenne, P.
In this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases.
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