Classification of Spanish extra virgin olive oils by data fusion of visible spectroscopic fingerprints and chemical descriptors

  1. Pizarro, C. 1
  2. Rodríguez-Tecedor, S. 1
  3. Pérez-Del-Notario, N. 1
  4. Esteban-Díez, I. 1
  5. González-Sáiz, J.M. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Food Chemistry

ISSN: 0308-8146

Año de publicación: 2013

Volumen: 138

Número: 2-3

Páginas: 915-922

Tipo: Artículo

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DOI: 10.1016/J.FOODCHEM.2012.11.087 SCOPUS: 2-s2.0-84873741106 WoS: WOS:000316039500029 GOOGLE SCHOLAR

Otras publicaciones en: Food Chemistry

Objetivos de desarrollo sostenible

Proyectos relacionados

DESARROLLO DE NUEVAS ESTRATÉGICAS QUIMIOMÉTRICAS PARA EL TRATAMIENTO DE DATOS INSTRUMENTALES INESPECÍFICOS.

2009/00014/001

Resumen

The potential of visible fingerprints and physical-chemical parameters in combination with multivariate data analysis was examined to classify extra virgin olive oils (EVOOs) from different Spanish regions according to their geographical origin. Firstly, spectral and quality parameters matrices were processed separately and subsequently were joined to evaluate the effect of synergy on the information obtained from the different methodologies. Linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) were performed as classification methods. The results revealed a perfect discrimination between the defined categories after performing PLS-DA on the Fused matrix, reaching 100% of correct classifications and showed a clear improvement in the overall prediction rates (92.5%), so that the effect of synergy was confirmed. These accurate results emphasise the feasibility of the proposed strategy and encourage the development of similar approaches based on visible spectroscopy in olive oil quality and traceability determination. © 2012 Elsevier Ltd. All rights reserved.