IQUIR   05412
INSTITUTO DE QUIMICA ROSARIO
Unidad Ejecutora - UE
artículos
Título:
Regression models based on new local strategies for near infrared spectroscopic data
Autor/es:
ALLEGRINI, F.; OLIVIERI, A. C.; FRAGOSO, W.; DARDENNE, P.; FERNANDEZ PIERNA, J.A.; BAETEN, V
Revista:
ANALYTICA CHIMICA ACTA
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Lugar: Amsterdam; Año: 2016 vol. 933 p. 50 - 58
ISSN:
0003-2670
Resumen:
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.