Multivariate analysis of HT/GC-(IT)MS chromatographic profiles of triacylglycerol for classification of olive oil varieties |
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Authors: | Cristina Ruiz-Samblás Luis Cuadros-Rodríguez Antonio González-Casado Francisco de Paula Rodríguez García Paulina de la Mata-Espinosa Juan Manuel Bosque-Sendra |
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Institution: | 1.Food Quality Control Service, Consejería de Agricultura y Pesca,Junta de Andalucía,Sevilla,Spain;2.Department of Analytical Chemistry,University of Granada,Granada,Spain |
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Abstract: | The ability of multivariate analysis methods such as hierarchical cluster analysis, principal component analysis and partial
least squares-discriminant analysis (PLS-DA) to achieve olive oil classification based on the olive fruit varieties from their
triacylglycerols profile, have been investigated. The variations in the raw chromatographic data sets of 56 olive oil samples
were studied by high-temperature gas chromatography with (ion trap) mass spectrometry detection. The olive oil samples were
of four different categories (“extra-virgin olive oil”, “virgin olive oil”, “olive oil” and “olive-pomace” oil), and for the
“extra-virgin” category, six different well-identified olive oil varieties (“hojiblanca”, “manzanilla”, “picual”, “cornicabra”,
“arbequina” and “frantoio”) and some blends of unidentified varieties. Moreover, by pre-processing methods of chemometric
(to linearise the response of the variables) such as peak-shifting, baseline (weighted least squares) and mean centering,
it was possible to improve the model and grouping between different varieties of olive oils. By using the first three principal
components, it was possible to account for 79.50% of the information on the original data. The fitted PLS-DA model succeeded
in classifying the samples. Correct classification rates were assessed by cross-validation. |
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