DETERMINING THE MOST EFFECTIVE COMBINATION OF CHEMICAL PARAMETERS FOR DIFFERENTIATING THE GEOGRAPHIC ORIGIN OF FOOD PRODUCTS: AN EXAMPLE USING COFFEE BEANS

Abstract

Numerous chemical measures have been explored as ways to discriminate the geographic origin of food products. The “best” set of measures to determine the provenance of a food product may vary depending on the spatial scale in question. Canonical analysis of principle coordinates was used to determine which chemical measure(s) provided the best ability to determine the provenance of green coffee beans sourced from nine international growing regions.  Models were constructed at two spatial scales. The chemical analyses used were the stable isotope ratios of carbon (δ¹³C), nitrogen (δ¹⁵N) and hydrogen (δ²H), concentrations of fatty acids, and concentrations of major, minor and trace elements. Variations in elemental concentrations provided the best predictor of whether a sample was from Kona, Hawaii or not (classification success rate 100%). Variability in elemental concentrations was also the single best discriminator of growing region of origin; however, the highest classification success, 86%, was achieved by elemental concentration data combined with δ¹³C and δ¹⁵N or δ²H. The statistical framework allows comparisons at multiple spatial scales to assist with decisions regarding which chemical analyses may be appropriate for the development of proof of origin methods for specific food and beverage products in the future.