Linear and non-linear modeling to identify vinegars in blends through spectroscopic data

LWT

Volumn 65, Issue , 2016, Pages 565-571

  Torrecilla, José S ^a   Aroca Santos, Regina ^a   Cancilla, John C ^a   Matute, Gemma ^a  

^a UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

Author keywords

Artificial neural network; Blends; Identification; Spectroscopy; Vinegar

Indexed keywords

ACETIC ACID; BLENDING; DISCRIMINANT ANALYSIS; IDENTIFICATION (CONTROL SYSTEMS); LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; SPECTROSCOPY; WINE;

CHEMOMETRIC TOOLS; CLASSIFICATION RATES; NON-LINEAR MODEL; NONLINEAR APPROACH; PARTIAL LEAST SQUARES DISCRIMINANT ANALYSES (PLSDA); SPECTROSCOPIC DATA; TRAINING FUNCTION; VINEGAR;

MULTILAYER NEURAL NETWORKS;

EID: 84951956604     PISSN: 00236438     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.lwt.2015.08.027     Document Type: Article

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