“…The artificial neural networks applied to visible spectroscopic data could be used to determine varietal quantifications based on the pigment profile of monovarietal extra virgin olive oils at 10% and 2.8% for the linear and non-linear models, respectively Aroca-Santosa, Cancilla, Pérez-Pérez, Moral, and Torrecilla (2016) Detection of possible fraud markers Ultra-high-performance liquid chromatography tandem mass spectrometry coupled with two types of atmospheric pressure ionizations (chemical and photoionization) were found efficient in determining natural color pigments (carotenoids, chlorophylls and chlorophyll derivatives) as well as artificial ones (E141i) in olive oils. These methods were applicable in pigment profile identification as well as the detection of possible exogenous adulterants Arrizabalaga-Larrañaga, Rodríguez, Medina, Santos, and Moyano (2019) Cultivar differentiation Five different Greek olive oil cultivars were successfully characterized and classified based on acidity, total chlorophylls and carotenoids, myristic, margaric, stearic, arachidic, and eicosenoic acids at a rate of 91.9% and 81.1% by using original and cross-validation methods, respectively Karabagias et al (2019) Geographical differentiation Geographical discrimination power of several chemical parameters (total phenol content, fatty acid and phenol profile, total carotene and chlorophyll content and oxidative stability) and mid-infrared spectroscopy on olive oils was investigated. It was found that combination of chemical parameters was better than mid-IR spectroscopy in classification of monovarietal olive oil obtained from geographically close regions of Turkey Uncu and Ozen (2016) Cultivar discrimination Monovarietal extra virgin olive oils could be classified up to 94.4% according to their variety by using Raman spectroscopy highly correlated with both carotenoid and fatty acid composition of olive oils.…”