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ABSTRACT
Over the last decades, olive oil quality and authenticity control has become an issue of great importance to consumers, suppliers, retailers, and regulators in both traditional and emerging olive oil producing countries, mainly due to the increasing worldwide popularity and the trade globalization of this product. Thus, in order to ensure olive oil authentication, various national and international laws and regulations have been adopted, although some of them are actually causing an enormous debate about the risk that they can represent for the harmonization of international olive oil trade standards. Within this context, this review was designed to provide a critical overview and comparative analysis of selected regulatory frameworks for olive oil authentication, with special emphasis on the quality and purity criteria considered by these regulation systems, their thresholds and the analytical methods employed for monitoring them. To complete the general overview, recent analytical advances to overcome drawbacks and limitations of the official methods to evaluate olive oil quality and to determine possible adulterations were reviewed. Furthermore, the latest trends on analytical approaches to assess the olive oil geographical and varietal origin traceability were also examined.
Abbreviations
| 1,2-DAGs | = | 1,2-diacylglycerols |
| 2P | = | 2-glyceryl monopalmitate |
| AOCS | = | American Oil Chemists' Society |
| AFLP | = | amplified fragment length polymorphism |
| APPI | = | atmospheric pressure photoionization ion sources |
| ATR-FTIR | = | attenuated total reflection Fourier transfer infrared |
| AUS | = | Australian Standards |
| CAF | = | Californian Standards |
| CAD | = | charged aerosol detector |
| CA | = | cluster analysis |
| CE | = | capillary electrophoresis |
| Codex | = | Codex Alimentarius |
| COPO | = | crude olive-pomace oil |
| DNA | = | deoxyribonucleic acid |
| DAGs | = | diacylglycerols |
| ∆ECN42 | = | difference between actual and theoretical content of triacylglycerols |
| DSC | = | differential scanning calorimetry |
| DART | = | direct analysis in real time |
| ESI | = | electrospray ionization |
| ECN | = | equivalent carbon number |
| EC | = | European Commission |
| EU | = | European Union |
| EVOO | = | extra virgin olive oil |
| FAAEs | = | fatty acid alkyl esters |
| FAEEs | = | fatty acids ethyl esters |
| FAMEs | = | fatty acids methyl esters |
| FAs | = | fatty acids |
| FID | = | flame ionization detector |
| FIA | = | Flow injection analysis |
| FLD | = | Fluorescence detector |
| FT-IR | = | Fourier transform infrared |
| FT-MIR | = | Fourier transform-mid-infrared |
| FT-Raman | = | Fourier transform-Raman |
| FFAs | = | free fatty acids |
| GC-EI MS | = | gas chromatography coupled to electron impact mass spectrometry |
| GC-O | = | GC-olfactory |
| GA-PLS | = | genetic algorithm partial least squares |
| HS-MS | = | headspace-mass spectrometry |
| HCA | = | hierarchical cluster analysis |
| H-PLS | = | hierarchical partial least-squares |
| HPLC | = | high pressure liquid chromatography |
| IOC | = | International Olive Council |
| ISO | = | International Organization for Standardization |
| ISSR | = | inter-simple sequence repeats |
| iPLS | = | interval partial least-squares |
| LVOO | = | lampante virgin olive oil |
| LASSO | = | least absolute shrinkage and selection operator |
| LDA | = | linear discriminant analysis |
| LR | = | linear regression |
| MS | = | mass spectrometry |
| MALDI | = | matrix-assisted laser desorption/ionization |
| MeD | = | median of olive oil defects |
| MeF | = | median of olive oil fruitiness |
| MIR | = | mid-infrared |
| MCUVE | = | Monte Carlo uninformative variable elimination |
| MLR | = | multiple linear regression |
| NCM | = | nearest class mean |
| N/A | = | not applicable |
| N/C | = | not considered |
| NIR | = | near-infrared |
| NMR | = | nuclear magnetic resonance |
| OO | = | olive oil |
| OPO | = | olive-pomace oil |
| ANOVA | = | one way analysis of variance |
| OVOO | = | ordinary virgin olive oil |
| PLS-DA | = | partial least squares discriminant analysis |
| PLS | = | partial least-squares |
| PBr | = | passing-bablok regression |
| PV | = | peroxide values |
| PCR | = | polymerase chain reaction |
| PCA | = | principal component analysis |
| PCS | = | principal component spectra diagnostic |
| PCr | = | principle component regression |
| PPPs | = | pyropheophytins |
| RAPD | = | random amplified polymorphic DNA |
| ROO | = | refined olive oil |
| ROPO | = | refined olive-pomace oil |
| OLS | = | ordinary least squares |
| SSR | = | simple sequence repeats |
| SNP | = | single nucleotide polymorphisms |
| SIMCA | = | soft independent modeling of class analogies |
| SPE | = | solid phase extraction |
| SPA | = | successive projections algorithm |
| SVM | = | support vector machine |
| TDR | = | time-domain reflectometry |
| TOF MS | = | time-of-flight mass spectrometry |
| TAGs | = | triacylglycerols |
| K232 and K268 or K270 | = | ultraviolet specific extinction coefficients |
| UV | = | Ultraviolet |
| USDA | = | United States Department of Agriculture |
| VOO | = | virgin olive oil |
| Vis/NIR | = | Visible/near infrared |
| Vis/Raman | = | Visible/Raman |
Acknowledgments
The authors want to express their sincere gratitude to the Spanish Agency for International Development Cooperation (AECID) (Pre-doctoral grant) and the Vice-Rector's Office for International Relations and Development Cooperation of the University of Granada.