Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7584860 | Food Chemistry | 2018 | 35 Pages |
Abstract
This work explores the potential of multi-element fingerprinting in combination with advanced data mining strategies to assess the geographical origin of extra virgin olive oil samples. For this purpose, the concentrations of 55 elements were determined in 125 oil samples from multiple Spanish geographic areas. Several unsupervised and supervised multivariate statistical techniques were used to build classification models and investigate the relationship between mineral composition of olive oils and their provenance. Results showed that Spanish extra virgin olive oils exhibit characteristic element profiles, which can be differentiated on the basis of their origin in accordance with three geographical areas: Atlantic coast (Huelva province), Mediterranean coast and inland regions. Furthermore, statistical modelling yielded high sensitivity and specificity, principally when random forest and support vector machines were employed, thus demonstrating the utility of these techniques in food traceability and authenticity research.
Keywords
ICP-OESPLS-DASPECEVOOPCASENSInductively coupled plasma optical emission spectrometryPartial least squares discriminant analysisLinear discriminant analysisPrincipal component analysisLDARandom forestSensitivityData miningGeographical traceabilityOlive oilExtra virgin olive oilinductively coupled plasma-mass spectrometryinductively coupled plasma mass spectrometryICP-MSSVMSupport vector machineMineral profileSpecificity
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Ana Sayago, Raúl González-DomÃnguez, Rafael Beltrán, Ángeles Fernández-Recamales,