Application of Cryoprobe 1H Nuclear Magnetic Resonance Spectroscopy and Multivariate Analysis for the Verification of Corsican Honey

• Published in: Journal of agricultural and food chemistry Vol. 56; no. 14; pp. 5451 - 5456
• Main Authors: Donarski, James A, Jones, Stephen A, Charlton, Adrian J
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 23.07.2008
• Subjects: accuracy; Alkaloids - analysis; Analysis of Variance; Analytical Methods; Biological and medical sciences; chemometrics; Confectionery products and chocolate industries, honey; Discriminant Analysis; European Union; food authenticity; Food industries; food quality; France; Fundamental and applied biological sciences. Psychology; genetic programming; geographical origin; Honey; Honey - analysis; Honey - classification; Magnetic Resonance Spectroscopy; mathematical models; metabolomics; Models, Theoretical; multivariate analysis; NMR; nuclear magnetic resonance spectroscopy; PLS; product authenticity; projection to latent structures-linear discriminant analysis; proton nuclear magnetic resonance spectroscopy; protons; provenance; spectral analysis; spectroscopy; standards of identity; traceability; trigonelline; two-stage genetic programming; validity; Corsica; France; Honey; NMR; genetic programming; chemometrics; metabolomics; PLS; food authenticity; geographical origin; Geographical origin; NMR spectrometry; Verification; Multivariate analysis; Genetics; Adulteration; Application; Chemometrics; Food
• ISSN: 0021-8561; 1520-5118
• DOI: 10.1021/jf072402x

Proton nuclear magnetic resonance spectroscopy (1H NMR) and multivariate analysis techniques have been used to classify honey into two groups by geographical origin. Honey from Corsica (Miel de Corse) was used as an example of a protected designation of origin product. Mathematical models were constructed to determine the feasibility of distinguishing between honey from Corsica and that from other geographical locations in Europe, using 1H NMR spectroscopy. Honey from 10 different regions within five countries was analyzed. 1H NMR spectra were used as input variables for projection to latent structures (PLS) followed by linear discriminant analysis (LDA) and genetic programming (GP). Models were generated using three methods, PLS-LDA, two-stage GP, and a combination of PLS and GP (PLS-GP). The PLS-GP model used variables selected by PLS for subsequent GP calculations. All models were generated using Venetian blind cross-validation. Overall classification rates for the discrimination of Corsican and non-Corsican honey of 75.8, 94.5, and 96.2% were determined using PLS-LDA, two-stage GP, and PLS-GP, respectively. The variables utilized by PLS-GP were related to their 1H NMR chemical shifts, and this led to the identification of trigonelline in honey for the first time.