Regression models based on new local strategies for near infrared spectroscopic data |
| |
| Authors: | F. Allegrini,J.A. Ferná ndez Pierna,W.D. Fragoso,A.C. Olivieri,V. Baeten,P. Dardenne |
| |
| Affiliation: | 1. Univ. Nacional de Rosario, Facultad de Ciencias Bioquímicas y Farmacéuticas, IQUIR, CONICET, Argentina;2. Valorisation of Agricultural Products Dpt, Walloon Agricultural Research Centre, Gembloux, Belgium;3. Departamento de Química, Universidade Federal da Paraíba, Campus I, João Pessoa, Brazil |
| |
| Abstract: | 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. |
| |
| Keywords: | Local regression models Near infrared spectroscopy Partial least squares regression |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
