title: Regression models based on new local strategies for near infrared spectroscopic data.
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.
authors: Univ. Nacional de Rosario, Facultad de Ciencias Bioquímicas y Farmacéuticas, IQUIR, CONICET, Argentina. Electronic address: allegrini@iquir-conicet.gov.ar.
authors: F
authors: F
authors: Allegrini
authors: Valorisation of Agricultural Products Dpt, Walloon Agricultural Research Centre, Gembloux, Belgium.
authors: J A
authors: JA
authors: Fernández Pierna
authors: Departamento de Química, Universidade Federal da Paraíba, Campus I, João Pessoa, Brazil.
authors: W D
authors: WD
authors: Fragoso
authors: Univ. Nacional de Rosario, Facultad de Ciencias Bioquímicas y Farmacéuticas, IQUIR, CONICET, Argentina.
authors: A C
authors: AC
authors: Olivieri
authors: Valorisation of Agricultural Products Dpt, Walloon Agricultural Research Centre, Gembloux, Belgium.
authors: V
authors: V
authors: Baeten
authors: Valorisation of Agricultural Products Dpt, Walloon Agricultural Research Centre, Gembloux, Belgium.
authors: P
authors: P
authors: Dardenne
mesh_terms: D000465:Algorithms; D016018:Least-Squares Analysis; D016014:Linear Models; D012639:Seeds / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared; D003313:Zea mays / Q000737:chemistry*
keywords: Local regression models; Near infrared spectroscopy; Partial least squares regression