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Food Analytical Methods

, Volume 12, Issue 1, pp 293–304 | Cite as

Fast Methodology for Identification of Olive Oil Adulterated with a Mix of Different Vegetable Oils

  • Paula Freitas Filoda
  • Lucas Flores Fetter
  • Franccesca Fornasier
  • Rosana de Cassia de Souza Schneider
  • Gilson Augusto Helfer
  • Bruna Tischer
  • Aline Teichmann
  • Adilson Ben da CostaEmail author
Article
  • 121 Downloads

Abstract

This paper investigated the application of Fourier transform infrared spectroscopy (FTIR) with partial least squares regression algorithms (PLS) and variable selection methods for the rapid identification of extra virgin olive oil (EVOO) adulterated with different vegetable oils. For this purpose, a unique calibration model was proposed for the identification and quantification of adulteration independent of the adulterating oil. Calibration models were developed for simultaneous determination of the concentration of oleic, linoleic and linolenic fatty acids. Robust models were also developed for quantification of the percentage of adulteration in the samples, independent of the adulterating oil. The calibration set consisted of 68 adulterated EVOO samples, prepared by the addition of vegetable oils (soybean, sunflower, corn, and canola oil) at different levels (1 to 80%, v/v), and ten commercial samples of EVOO were used to validate the models. The mid-infrared spectra were recorded in the wave number range 3200 to 650 cm−1 for all samples. Chromatographic analysis was performed to determine the fatty acid profile of the samples from the calibration and prediction sets. PLS models were developed and different strategies were investigated during the preprocessing of the IR spectra. The results of the prediction were compared with those obtained from the conventional analysis, and RMSEP values of 4.44, 1.92, and 0.62% were obtained for oleic, linoleic, and linolenic acids, respectively. The proposed methodology allowed us to quantify the acids simultaneously in the samples and also demonstrated a good predictive power to identify the adulterated samples.

Keywords

Extra virgin olive oil Adulteration FTIR Partial least squares model Variable selection Green analytical chemistry 

Notes

Acknowledgments

The authors would like to thank Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

Compliance with Ethical Standards

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

This article does not contain any studies with human or animal subjects.

Informed Consent

Publication has been approved by all individual participants.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Paula Freitas Filoda
    • 1
  • Lucas Flores Fetter
    • 1
  • Franccesca Fornasier
    • 2
  • Rosana de Cassia de Souza Schneider
    • 2
    • 3
  • Gilson Augusto Helfer
    • 4
  • Bruna Tischer
    • 5
  • Aline Teichmann
    • 1
  • Adilson Ben da Costa
    • 1
    • 2
    Email author return OK on get
  1. 1.Programa de Pós-Graduação em Sistema e Processos IndustriaisSanta Cruz do SulBrazil
  2. 2.Departamento de Química e FisicaSanta Cruz do SulBrazil
  3. 3.Programa de Pós-Graduação em Tecnologia AmbientalSanta Cruz do SulBrazil
  4. 4.Departamento de ComputaçãoUniversidade de Santa Cruz do SulSanta Cruz do SulBrazil
  5. 5.Instituto de Ciência e Tecnologia de AlimentosUniversidade Federal do Rio Grande do SulPorto AlegreBrazil

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