LAUSR

Abstract Traceability of food has become an important issue. In particular, the traceability of natural products has become an important topic considering the high number of possible sources of environmental pollutants which these products are exposed during the production process. Chemical composition and organoleptic characteristics of honey are strongly influenced by its geographical origin. Currently, multivariate statistical techniques applied to multielement data are used to identify geographical origin in different areas of food chemistry. These techniques can identify natural clustering patterns or find discriminant functions for predictive models. In this study, the results of multielement analysis were used to verify the geographical origin of honeys produced in provinces of the Northeast of Argentina. Thus, a chemometric model was developed and validated to authenticate Argentinean honeys based on the chemical composition using multivariate techniques. The linear discriminant analysis (LDA) with multielement data of honey samples from the Northeast region of Argentina classified honey in three groups. The leave-one-out cross-validation method correctly classified 76% of samples. Additionally, LDA using multielement data fused with physicochemical data correctly classified 94% of samples.