Differentiating pasture honey from eucalyptus honey based on carbon isotopic data in Uruguay

To avoid false declarations of geographic and botanical honey origin, traceability should be based on analytical data, which could then be processed by multivariate statistical methods. Obtaining this data, however, is costly and time consuming. Thus, it would be more convenient to acquire this information from routine trials, for example from the analysis for determination of high fructose corn syrup (HFCS) concentration in honey. The availability of a procedure of this kind in Uruguay would be useful in discriminating between honeys from grasslands to that from eucalyptus, the two main floral sources for commercial production. To this effect, honey samples (47 from pastures and 42 from eucalypts) were analyzed for δ(13)C in both honey and its protein fraction. We identified a logistic regression model that allowed us to correctly assign 90% of the training samples, using δ(13)C data of honey, protein fraction, and the isotopic index as variables. This model was then validated, obtaining 100% correct allocation for honeys from pasture and 90% for honeys from eucalyptus. Moreover, we found that this information could also be used to establish adulteration with HFCS based on a local stricter cut-off limit than that of -1.0‰ of the international index.