Use of artificial neural networks in near-infrared reflectance spectroscopy calibrations for predicting the inclusion percentages of wheat and sunflower meal in compound feedingstuffs

Applied Spectroscopy

Volumn 60, Issue 9, 2006, Pages 1062-1069

  Perez Marin D ^a   Garrido Varo, A ^a   Guerrero, J E ^a   Gutierrez Estrada J C ^b  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b UNIVERSITY OF HUELVA   (Spain)

Author keywords

ANN; Artificial neural networks; Compound feedingstuffs; Ingredient percentage; Near infrared reflectance spectroscopy; NIRS

Indexed keywords

CALIBRATION; NEURAL NETWORKS; NONLINEAR SYSTEMS; SAMPLING;

COMPOUND FEEDINGSTUFFS; INGREDIENT PERCENTAGE; NEAR INFRARED REFLECTANCE SPECTROSCOPY (NIRS); RADIAL BASIS FUNCTION NETS (RBF);

AGRICULTURE;

ANIMAL FOOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMISTRY; METHODOLOGY; NEAR INFRARED SPECTROSCOPY; REGRESSION ANALYSIS; SUNFLOWER; WHEAT;

ANIMAL FEED; HELIANTHUS; NEURAL NETWORKS (COMPUTER); REGRESSION ANALYSIS; SPECTROSCOPY, NEAR-INFRARED; TRITICUM;

HELIANTHUS; NEURAL NETWORKS; NONLINEAR PROGRAMING; SAMPLING; WHEAT;

EID: 33748920862     PISSN: 00037028     EISSN: None     Source Type: Journal    
DOI: 10.1366/000370206778397506     Document Type: Article

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