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Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor.
J AOAC Int. 2021 Mar 05; 104(1):7-15.JA

Abstract

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.

Links

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Authors+Show Affiliations

Weesepoel Y
Wageningen Food Safety Research, P.O. Box 230, Wageningen, The Netherlands, 6700 AE.
Alewijn M
Wageningen Food Safety Research, P.O. Box 230, Wageningen, The Netherlands, 6700 AE.
Wijtten M
Wageningen Food Safety Research, P.O. Box 230, Wageningen, The Netherlands, 6700 AE.
Müller-Maatsch J
Wageningen Food Safety Research, P.O. Box 230, Wageningen, The Netherlands, 6700 AE.

MeSH

FraudOlive OilOptics and PhotonicsPlant OilsSpectroscopy, Near-Infrared

Pub Type(s)

Journal Article

Language

eng

PubMed ID

33259580

Citation

Weesepoel, Yannick, et al. "Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor." Journal of AOAC International, vol. 104, no. 1, 2021, pp. 7-15.
Weesepoel Y, Alewijn M, Wijtten M, et al. Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor. J AOAC Int. 2021;104(1):7-15.
Weesepoel, Y., Alewijn, M., Wijtten, M., & Müller-Maatsch, J. (2021). Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor. Journal of AOAC International, 104(1), 7-15. https://doi.org/10.1093/jaoacint/qsaa099
Weesepoel Y, et al. Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor. J AOAC Int. 2021 Mar 5;104(1):7-15. PubMed PMID: 33259580.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor. AU - Weesepoel,Yannick, AU - Alewijn,Martin, AU - Wijtten,Michiel, AU - Müller-Maatsch,Judith, PY - 2020/05/29/received PY - 2020/07/14/accepted PY - 2020/12/2/pubmed PY - 2021/6/29/medline PY - 2020/12/1/entrez SP - 7 EP - 15 JF - Journal of AOAC International JO - J AOAC Int VL - 104 IS - 1 N2 - 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. SN - 1944-7922 UR - https://www.unboundmedicine.com/medline/citation/33259580/Detecting_Food_Fraud_in_Extra_Virgin_Olive_Oil_Using_a_Prototype_Portable_Hyphenated_Photonics_Sensor_ DB - PRIME DP - Unbound Medicine ER -