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Hyperspectral imaging and chemometrics for authentication of extra virgin olive oil : a comparative approach with FTIR, UV-VIS, Raman, and GC-MS

Derick Malavi (UGent) , Amin Nikkhah (UGent) , Katleen Raes (UGent) and Sam Van Haute (UGent)
(2023) FOODS. 12(3).
Author
Organization
Abstract
Limited information on monitoring adulteration in extra virgin olive oil (EVOO) by hyperspectral imaging (HSI) exists. This work presents a comparative study of chemometrics for the authentication and quantification of adulteration in EVOO with cheaper edible oils using GC-MS, HSI, FTIR, Raman and UV-Vis spectroscopies. The adulteration mixtures were prepared by separately blending safflower oil, corn oil, soybean oil, canola oil, sunflower oil, and sesame oil with authentic EVOO in different concentrations (0-20%, m/m). Partial least squares-discriminant analysis (PLS-DA) and PLS regression models were then built for the classification and quantification of adulteration in olive oil, respectively. HSI, FTIR, UV-Vis, Raman, and GC-MS combined with PLS-DA achieved correct classification accuracies of 100%, 99.8%, 99.6%, 96.6%, and 93.7%, respectively, in the discrimination of authentic and adulterated olive oil. The overall PLS regression model using HSI data was the best in predicting the concentration of adulterants in olive oil with a low root mean square error of prediction (RMSEP) of 1.1%, high R-pred(2) (0.97), and high residual predictive deviation (RPD) of 6.0. The findings suggest the potential of HSI technology as a fast and non-destructive technique to control fraud in the olive oil industry.
Keywords
Plant Science, Health Professions (miscellaneous), Health (social science), Microbiology, Food Science, hyperspectral imaging, extra virgin olive oil, adulteration, authenticity, edible oils, SOYBEAN OIL, SPECTROSCOPY, ADULTERATION, NIR, QUANTIFICATION, FLUORESCENCE, COMBINATION, QUALITY, BLENDS, ATR

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Citation

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MLA
Malavi, Derick, et al. “Hyperspectral Imaging and Chemometrics for Authentication of Extra Virgin Olive Oil : A Comparative Approach with FTIR, UV-VIS, Raman, and GC-MS.” FOODS, vol. 12, no. 3, 2023, doi:10.3390/foods12030429.
APA
Malavi, D., Nikkhah, A., Raes, K., & Van Haute, S. (2023). Hyperspectral imaging and chemometrics for authentication of extra virgin olive oil : a comparative approach with FTIR, UV-VIS, Raman, and GC-MS. FOODS, 12(3). https://doi.org/10.3390/foods12030429
Chicago author-date
Malavi, Derick, Amin Nikkhah, Katleen Raes, and Sam Van Haute. 2023. “Hyperspectral Imaging and Chemometrics for Authentication of Extra Virgin Olive Oil : A Comparative Approach with FTIR, UV-VIS, Raman, and GC-MS.” FOODS 12 (3). https://doi.org/10.3390/foods12030429.
Chicago author-date (all authors)
Malavi, Derick, Amin Nikkhah, Katleen Raes, and Sam Van Haute. 2023. “Hyperspectral Imaging and Chemometrics for Authentication of Extra Virgin Olive Oil : A Comparative Approach with FTIR, UV-VIS, Raman, and GC-MS.” FOODS 12 (3). doi:10.3390/foods12030429.
Vancouver
1.
Malavi D, Nikkhah A, Raes K, Van Haute S. Hyperspectral imaging and chemometrics for authentication of extra virgin olive oil : a comparative approach with FTIR, UV-VIS, Raman, and GC-MS. FOODS. 2023;12(3).
IEEE
[1]
D. Malavi, A. Nikkhah, K. Raes, and S. Van Haute, “Hyperspectral imaging and chemometrics for authentication of extra virgin olive oil : a comparative approach with FTIR, UV-VIS, Raman, and GC-MS,” FOODS, vol. 12, no. 3, 2023.
@article{01GS9BC3KXMNM57Y6555TG4F8G,
  abstract     = {{Limited information on monitoring adulteration in extra virgin olive oil (EVOO) by hyperspectral imaging (HSI) exists. This work presents a comparative study of chemometrics for the authentication and quantification of adulteration in EVOO with cheaper edible oils using GC-MS, HSI, FTIR, Raman and UV-Vis spectroscopies. The adulteration mixtures were prepared by separately blending safflower oil, corn oil, soybean oil, canola oil, sunflower oil, and sesame oil with authentic EVOO in different concentrations (0-20%, m/m). Partial least squares-discriminant analysis (PLS-DA) and PLS regression models were then built for the classification and quantification of adulteration in olive oil, respectively. HSI, FTIR, UV-Vis, Raman, and GC-MS combined with PLS-DA achieved correct classification accuracies of 100%, 99.8%, 99.6%, 96.6%, and 93.7%, respectively, in the discrimination of authentic and adulterated olive oil. The overall PLS regression model using HSI data was the best in predicting the concentration of adulterants in olive oil with a low root mean square error of prediction (RMSEP) of 1.1%, high R-pred(2) (0.97), and high residual predictive deviation (RPD) of 6.0. The findings suggest the potential of HSI technology as a fast and non-destructive technique to control fraud in the olive oil industry.}},
  articleno    = {{429}},
  author       = {{Malavi, Derick and Nikkhah, Amin and Raes, Katleen and Van Haute, Sam}},
  issn         = {{2304-8158}},
  journal      = {{FOODS}},
  keywords     = {{Plant Science,Health Professions (miscellaneous),Health (social science),Microbiology,Food Science,hyperspectral imaging,extra virgin olive oil,adulteration,authenticity,edible oils,SOYBEAN OIL,SPECTROSCOPY,ADULTERATION,NIR,QUANTIFICATION,FLUORESCENCE,COMBINATION,QUALITY,BLENDS,ATR}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{21}},
  title        = {{Hyperspectral imaging and chemometrics for authentication of extra virgin olive oil : a comparative approach with FTIR, UV-VIS, Raman, and GC-MS}},
  url          = {{http://doi.org/10.3390/foods12030429}},
  volume       = {{12}},
  year         = {{2023}},
}
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