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Article

Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods

by
Ian W. Burton
1,
Mohsen Kompany-Zareh
2,
Sophie Haverstock
1,
Jonathan Haché
3,
Camilo F. Martinez-Farina
1,
Peter D. Wentzell
2 and
Fabrice Berrué
1,*
1
Aquatic and Crop Resource Development, National Research Council of Canada, Halifax, NS B3H 3Z1, Canada
2
Trace Analysis Research Centre, Department of Chemistry, Dalhousie University, P.O. Box 15000, Halifax, NS B3H 4R2, Canada
3
Canadian Food Inspection Agency, 1400 Merivale Rd, Ottawa, ON K1A 0Y9, Canada
*
Author to whom correspondence should be addressed.
Molecules 2023, 28(4), 1656; https://doi.org/10.3390/molecules28041656
Submission received: 11 January 2023 / Revised: 2 February 2023 / Accepted: 6 February 2023 / Published: 9 February 2023
(This article belongs to the Special Issue Application of Spectroscopy and Chemometrics for Authentication of Foods and Drugs)
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Abstract

To address the growing concern of honey adulteration in Canada and globally, a quantitative NMR method was developed to analyze 424 honey samples collected across Canada as part of two surveys in 2018 and 2019 led by the Canadian Food Inspection Agency. Based on a robust and reproducible methodology, NMR data were recorded in triplicate on a 700 MHz NMR spectrometer equipped with a cryoprobe, and the data analysis led to the identification and quantification of 33 compounds characteristic of the chemical composition of honey. The high proportion of Canadian honey in the library provided a unique opportunity to apply multivariate statistical methods including PCA, PLS-DA, and SIMCA in order to differentiate Canadian samples from the rest of the world. Through satisfactory model validation, both PLS-DA as a discriminant modeling technique and SIMCA as a class modeling method proved to be reliable at differentiating Canadian honey from a diverse set of honeys with various countries of origins and floral types. The replacement method of optimization was successfully applied for variable selection, and trigonelline, proline, and ethanol at a lower extent were identified as potential chemical markers for the discrimination of Canadian and non-Canadian honeys.
Keywords: nuclear magnetic resonance (NMR); honey; quantitation; multivariate statistical analyses; food adulteration nuclear magnetic resonance (NMR); honey; quantitation; multivariate statistical analyses; food adulteration

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MDPI and ACS Style

Burton, I.W.; Kompany-Zareh, M.; Haverstock, S.; Haché, J.; Martinez-Farina, C.F.; Wentzell, P.D.; Berrué, F. Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods. Molecules 2023, 28, 1656. https://doi.org/10.3390/molecules28041656

AMA Style

Burton IW, Kompany-Zareh M, Haverstock S, Haché J, Martinez-Farina CF, Wentzell PD, Berrué F. Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods. Molecules. 2023; 28(4):1656. https://doi.org/10.3390/molecules28041656

Chicago/Turabian Style

Burton, Ian W., Mohsen Kompany-Zareh, Sophie Haverstock, Jonathan Haché, Camilo F. Martinez-Farina, Peter D. Wentzell, and Fabrice Berrué. 2023. "Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods" Molecules 28, no. 4: 1656. https://doi.org/10.3390/molecules28041656

APA Style

Burton, I. W., Kompany-Zareh, M., Haverstock, S., Haché, J., Martinez-Farina, C. F., Wentzell, P. D., & Berrué, F. (2023). Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods. Molecules, 28(4), 1656. https://doi.org/10.3390/molecules28041656

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