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Chemometric authentication of the organic status of milk on the basis of trace element content.
Rodríguez-Bermúdez, R; López-Alonso, M; Miranda, M; Fouz, R; Orjales, I; Herrero-Latorre, C.
Afiliação
  • Rodríguez-Bermúdez R; Departamento de Patología Animal, Facultad de Veterinaria, Universidade de Santiago de Compostela, 27002 Lugo, Spain. Electronic address: ruth.rodriguez@usc.es.
  • López-Alonso M; Departamento de Patología Animal, Facultad de Veterinaria, Universidade de Santiago de Compostela, 27002 Lugo, Spain. Electronic address: marta.lopez.alonso@usc.es.
  • Miranda M; Departamento de Anatomía, Producción Animal y Ciencias Clínicas Veterinarias, Facultad de Veterinaria, Universidade de Santiago de Compostela, 27002 Lugo, Spain. Electronic address: marta.miranda@usc.es.
  • Fouz R; Africor Lugo, Ronda de Fingoi, 117, 27002 Lugo, Spain. Electronic address: xerencia@africorlugo.com.
  • Orjales I; Departamento de Anatomía, Producción Animal y Ciencias Clínicas Veterinarias, Facultad de Veterinaria, Universidade de Santiago de Compostela, 27002 Lugo, Spain. Electronic address: inmaculada.orjales@usc.es.
  • Herrero-Latorre C; Departamento de Química Analítica, Nutrición y Bromatología, Facultad de Ciencias, Universidad de Santiago de Compostela, Campus de Lugo, 27002 Lugo, Spain. Electronic address: carlos.herrero@usc.es.
Food Chem ; 240: 686-693, 2018 Feb 01.
Article em En | MEDLINE | ID: mdl-28946330
ABSTRACT
The objective of this study was to develop a method for authenticating organic milk samples in North Spain on the basis of its trace mineral composition. Fourteen elements in 98 samples were determined by inductively coupled plasma mass spectrometry. Although concentrations of Co, Cr, Cu, I, Se and Zn where statistically higher in conventional milk and As in organic, none of these elements by itself was able to discriminate between organic and conventional milk. The chemical data was examined by principal component analysis and cluster analysis, revealing a natural separation between organic and conventional milk. In a second step, several supervised pattern recognition techniques were used to construct mathematical models for predicting the type of milk (organic or conventional) based on the metal content. The results proved that the model constructed using the artificial neural network is capable of correctly identifying the type of milk in almost 95% of cases.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Leite Tipo de estudo: Prognostic_studies Limite: Animals País/Região como assunto: Europa Idioma: En Revista: Food Chem Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Leite Tipo de estudo: Prognostic_studies Limite: Animals País/Região como assunto: Europa Idioma: En Revista: Food Chem Ano de publicação: 2018 Tipo de documento: Article