Optical spectroscopic analysis for the discrimination of Extra-Virgin Olive Oil
Abstract
We demonstrate the ability to discriminate between five brands of commercially available extra-virgin olive oil (EVOO) using Raman spectroscopy or fluorescence spectroscopy. Data was taken on both a ‘bulk optics’ free space system and on a compact handheld device, each capable of taking both Raman and fluorescence data. With the compact Raman device we achieved an average sensitivity and specificity of 98.4% and 99.6% for discrimination, respectively. Our approach illustrates that both Raman and fluorescence spectroscopy can be used for portable discrimination of EVOOs. This technique may enable detection of EVOO that has undergone counterfeiting or adulteration. The main challenge with this technique is that oxidation of EVOO causes a shift in the Raman signal over time. It would therefore be necessary to retrain the database regularly.We demonstrate preliminary data to address this issue, which may enable successful discrimination over time. We show that by discarding the first principal component, which contains information on the variations due to oxidation, we can improve discrimination efficiency.
Citation
McReynolds , N , Auñón Garcia , J M , Guengerich , Z , Smith , T K & Dholakia , K 2016 , ' Optical spectroscopic analysis for the discrimination of Extra-Virgin Olive Oil ' , Applied Spectroscopy , vol. 70 , no. 11 , pp. 1872-1882 . https://doi.org/10.1177/0003702816645931
Publication
Applied Spectroscopy
Status
Peer reviewed
ISSN
0003-7028Type
Journal article
Rights
© 2016, the Author(s). This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at asp.sagepub.com / https://doi.org/10.1177/0003702816645931
Description
We thank the UK Engineering and Physical Sciences Research Council and the European Union FAMOS project (FP7 ICT, 317744) for funding.Collections
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