Background: Current developments in portable photonic devices for fast authentication of extra virgin olive oil (EVOO) or EVOO with non-EVOO additions steer towards hyphenation of different optic technologies. The multiple spectra or so-called "fingerprints" of samples are then analyzed with multivariate statistics. For EVOO authentication, one-class classification (OCC) to identify "out-of-class" EVOO samples in combination with data-fusion is applicable.
Objective: Prospecting the application of a prototype photonic device ("PhasmaFood") which hyphenates visible, fluorescence, and near-infrared spectroscopy in combination with OCC modelling to classify EVOOs and discriminate them from other edible oils and adulterated EVOOs.
Method: EVOOs were adulterated by mixing in 10-50% (v/v) of refined and virgin olive oils, olive-pomace olive oils, and other common edible oils. Samples were analyzed by the hyphenated sensor. OCC, data-fusion, and decision thresholds were applied and optimized for two different scenarios.
Results: By high-level data-fusion of the classification results from the three spectral databases and several multivariate model vectors, a 100% correct classification of all pure edible oils using OCC in the first scenario was found. Reducing samples being falsely classified as EVOOs in a second scenario, 97% of EVOOs adulterated with non-EVOO olive oils were correctly identified and ones with other edible oils correctly classified at score of 91%.
Conclusions: Photonic sensor hyphenation in combination with high-level data fusion, OCC, and tuned decision thresholds delivers significantly better screening results for EVOO compared to individual sensor results.
Highlights: Hyphenated photonics and its data handling solutions applied to extra virgin olive oil authenticity testing was found to be promising.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372135 | PMC |
| http://dx.doi.org/10.1093/jaoacint/qsaa099 | DOI Listing |
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