LAPSE:2023.3184
Published Article

LAPSE:2023.3184
Assessment of Wine Adulteration Using Near Infrared Spectroscopy and Laser Backscattering Imaging
February 22, 2023
Abstract
Food adulteration is in the focus of research due to its negative effect on safety and nutritional value and because of the demand for the protection of brands and regional origins. Portugieser and Sauvignon Blanc wines were selected for experiments. Samples were made by water dilution, the addition of sugar and then a combination of both. Near infrared (NIR) spectra were acquired in the range of 900−1700 nm. Partial least squares regression was performed to predict the adulteration level. The model including all wines and adulterations achieved a prediction error of 0.59% added sugar and 6.85% water dilution. Low-power laser modules were used to collect diffuse reflectance signals at wavelengths of 532, 635, 780, 808, 850, 1064 nm. The general linear model resulted in a higher prediction error of 3.06% added sugar and 20.39% water dilution. Instead of classification, the present study investigated the feasibility of non-destructive methods in the prediction of adulteration level. Laser scattering successfully detected the added sugar with linear discriminant analysis (LDA), but its prediction accuracy was low. NIR spectroscopy might be suitable for rapid non-destructive estimation of wine adulteration.
Food adulteration is in the focus of research due to its negative effect on safety and nutritional value and because of the demand for the protection of brands and regional origins. Portugieser and Sauvignon Blanc wines were selected for experiments. Samples were made by water dilution, the addition of sugar and then a combination of both. Near infrared (NIR) spectra were acquired in the range of 900−1700 nm. Partial least squares regression was performed to predict the adulteration level. The model including all wines and adulterations achieved a prediction error of 0.59% added sugar and 6.85% water dilution. Low-power laser modules were used to collect diffuse reflectance signals at wavelengths of 532, 635, 780, 808, 850, 1064 nm. The general linear model resulted in a higher prediction error of 3.06% added sugar and 20.39% water dilution. Instead of classification, the present study investigated the feasibility of non-destructive methods in the prediction of adulteration level. Laser scattering successfully detected the added sugar with linear discriminant analysis (LDA), but its prediction accuracy was low. NIR spectroscopy might be suitable for rapid non-destructive estimation of wine adulteration.
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Keywords
chemometry, food adulteration, laser backscattering, wine authenticity
Subject
Suggested Citation
Hencz A, Nguyen LLP, Baranyai L, Albanese D. Assessment of Wine Adulteration Using Near Infrared Spectroscopy and Laser Backscattering Imaging. (2023). LAPSE:2023.3184
Author Affiliations
Hencz A: Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary
Nguyen LLP: Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary; Institute of Biotechnology and Food Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh 700000, Vietnam [ORCID]
Baranyai L: Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary [ORCID]
Albanese D: Department of Industrial Engineering, University of Salerno, 84084 Salerno, Italy [ORCID]
Nguyen LLP: Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary; Institute of Biotechnology and Food Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh 700000, Vietnam [ORCID]
Baranyai L: Institute of Food Science and Technology, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary [ORCID]
Albanese D: Department of Industrial Engineering, University of Salerno, 84084 Salerno, Italy [ORCID]
Journal Name
Processes
Volume
10
Issue
1
First Page
95
Year
2022
Publication Date
2022-01-04
ISSN
2227-9717
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Original Submission
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PII: pr10010095, Publication Type: Journal Article
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LAPSE:2023.3184
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https://doi.org/10.3390/pr10010095
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Feb 22, 2023
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