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Type:	Artigo de Periódico
Title:	Comparison of different multivariate classification methods for the detection of adulterations in grape nectars by using low-field nuclear magnetic resonance
Authors:	Carolina Sheng Whei Miaw Poliana Macedo Santos Alessandro Rangel Carolino Sales Silva Aline Gozzi Nilson César Castanheira Guimarães Maria Pilar Callao Itziar Ruisánchez Marcelo Martins de Sena Scheilla Vitorino Carvalho de Souza
Abstract:	Grape is the most consumed nectar in Brazil and a relatively expensive beverage. Therefore, it is susceptible to fraud by substitution with other less expensive fruit juices. Adulterations of grape nectars by the addition of apple juice, cashew juice, and mixtures of both were evaluated by using low-field nuclear magnetic resonance (LF-NMR) and supervised multivariate classification methods. Two different approaches were investigated using one-class (only unadulterated samples (UN) were modeled) and multiclass (three classes were modeled: UN, adulterated with cashew (CAS), and adulterated with apple (APP)) strategies. For the one-class approach, soft independent modeling of class analogy (SIMCA), one-class partial least squares (OCPLS), and data-driven SIMCA (DD-SIMCA) models were built. For the multiclass approach, partial least squares discriminant analysis (PLS-DA) and multiclass SIMCA models were built. The results obtained demonstrated good performances by all the one-class methods with efficiency rates higher than 93%. For the multiclass approach, the classification of samples containing only one type of adulterant presented efficiencies higher than 90% and 97% using SIMCA and PLS-DA, respectively. The classification of samples containing blends of two adulterants was satisfactory for the CAS class, but not for the APP class when applying PLS-DA. Nevertheless, multiclass SIMCA did not provide satisfactory predictions for either of these two classes.
Subject:	Tecnologia de alimentos Uva Alteração em néctar de uva Ressonância magnética nuclear de baixo campo Néctar de fruta
language:	eng
metadata.dc.publisher.country:	Brasil
Publisher:	Universidade Federal de Minas Gerais
Publisher Initials:	UFMG
metadata.dc.publisher.department:	FAR - DEPARTAMENTO DE ALIMENTOS ICX - DEPARTAMENTO DE QUÍMICA
Rights:	Acesso Restrito
metadata.dc.identifier.doi:	10.1007/s12161-019-01522-7
URI:	http://hdl.handle.net/1843/41830
Issue Date:	2020
metadata.dc.url.externa:	https://link.springer.com/article/10.1007/s12161-019-01522-7
metadata.dc.relation.ispartof:	Food Analytical Methods
Appears in Collections:	Artigo de Periódico

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