Chemometric discrimination of extra virgin olive oils (EVOO) from whole and stoned olive pastes was carried out by using Fourier transform infrared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach. Four Italian commercial EVOO brands, all in both whole and stoned version, were considered in this study. The adopted chemometric methodologies were able to describe the different chemical features in phenolic and volatile compounds contained in the two types of oil by using unspecific IR spectral information. Principal component analysis (PCA) was employed in cluster analysis to capture data patterns and to highlight differences between technological processes and EVOO brands. The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extraction procedures. Discriminant analysis was extended to the evaluation of possible adulteration by addition of aliquots of oil from whole paste to the most valuable oil from stoned olives. The statistical parameters from external validation of all the PLS models were very satisfactory, with low root mean square error of prediction (RMSEP) and relative error (RE%).

Chemometric analysis for discrimination of extra virgin olive oils from whole and stoned olive pastes

De Luca M
;
Restuccia D;Puoci F;Ragno G
2016

Abstract

Chemometric discrimination of extra virgin olive oils (EVOO) from whole and stoned olive pastes was carried out by using Fourier transform infrared (FTIR) data and partial least squares-discriminant analysis (PLS1-DA) approach. Four Italian commercial EVOO brands, all in both whole and stoned version, were considered in this study. The adopted chemometric methodologies were able to describe the different chemical features in phenolic and volatile compounds contained in the two types of oil by using unspecific IR spectral information. Principal component analysis (PCA) was employed in cluster analysis to capture data patterns and to highlight differences between technological processes and EVOO brands. The PLS1-DA algorithm was used as supervised discriminant analysis to identify the different oil extraction procedures. Discriminant analysis was extended to the evaluation of possible adulteration by addition of aliquots of oil from whole paste to the most valuable oil from stoned olives. The statistical parameters from external validation of all the PLS models were very satisfactory, with low root mean square error of prediction (RMSEP) and relative error (RE%).
Virgin olive oil, Chemometrics, Partial least square discriminant analysis, Principal component analysis, Fourier transform infrared, Stoned olive pastes
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/145918
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