Improving NIRS predictions of ingredient composition in compound feedingstuffs using Bayesian non-parametric calibrations

Chemometrics and Intelligent Laboratory Systems

Volumn 110, Issue 1, 2012, Pages 108-112

  Perez Marin D ^a   Fearn, T ^b   Guerrero, J E ^a   Garrido Varo, A ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Bayesian statistics; Compound feeds; Ingredient percentage; Near infrared reflectance spectroscopy; Nonlinear calibration

Indexed keywords

ANIMAL FOOD; ARTICLE; BAYES THEOREM; CALIBRATION; CONTROLLED STUDY; FOOD ANALYSIS; FOOD COMPOSITION; INTERMETHOD COMPARISON; NEAR INFRARED REFLECTANCE SPECTROSCOPY; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; VALIDATION PROCESS;

EID: 83655163724     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2011.10.007     Document Type: Article

References (13)

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