4.7 Article

Quality authentication of virgin olive oils using orthogonal techniques and chemometrics based on individual and high-level data fusion information

期刊

TALANTA
卷 219, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.talanta.2020.121260

关键词

Ion mobility spectrometry; Capillary electrophoresis; High-performance liquid chromatography; Volatile and polar fraction

资金

  1. Innolivar Project (Public Purchase of Innovation in its modality of Pre-Commercial Public Purchase)
  2. University of Cordoba (Spain)
  3. Spanish MICINN [PGC2018-098363-B-I00]
  4. FEDER funds, within the Pluriregional Operational Program of Spain 2014-2020
  5. Spanish Ministry of Education, Culture and Sport
  6. Ministry of Science, Innovation and Universities

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Currently, extra virgin olive oil, virgin olive oil and lampante olive oil are classified using physical-chemical analyses and a sensory analysis of fruitiness and defects, which is carried out by expert panels. This manual analysis is nowadays considered to be controversial and therefore analytical methodologies, which may be automated to classify these samples, are needed. In this work, we propose using an analytical platform based on two orthogonal techniques to determine the flavour components perceived in the mouth and the components contributing to the olive oils (OOs) aroma, respectively. For the former, capillary electrophoresis with ultraviolet detector (CE-UV) and high-performance liquid chromatography with UV or fluorescence detection were explored. The CE-UV analysis provided better results with the developed chemometric models (principal component analysis, linear discriminant analysis and k-nearest neighbors method). While for the latter, headspace (HS) - gas chromatography coupling with ion mobility spectrometry (GC-IMS) was selected due to the easy applicability of this technique to classify OOs. Then both techniques, CE-UV and GC-IMS, were selected to be integrated into one analytical platform. The potential of using both complementary/orthogonal techniques was demonstrated using high-level data fusion of CE-UV and GC-IMS data.

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