Project abstract

At EU level the demanded honey quantities are not satisfied by internal production. As a result, significant quantities are imported from other countries outside EU (i.e. China, Argentina) which need a rigorous control, honey being the third most adulterated product in the world. Its price tendency will be directly reflected in the rarity of a certain honey type, either in terms of geographical or botanical origin. In this case, the temptation of passing some counterfeit, cheap honeys under the label of some exclusive ones, in order to gain an illegal profit exists among some sellers or unfair producers.

In this context, “Honeyomics” project aims the development of new chemometric models able to differentiate among honeys with distinct geographical and botanical origin and also to detect honey adulteration through the addition of cheaper honey types to much rare honey varieties. Metabolomic approches based on vibrational (IR, Raman) and fluorescence spectroscopies will be developed, their classification potential being compared and validated based on acknowledged methods like isotope and elemental profiling. The large generated experimental data sets will be processed using supervised chemometric techniques (LDA and SIMCA) as well as artificiall intelligence (Machine Learning). The development of predictive models able to differentiate among distinct honey varieties and to identify adulterated samples using Machine Learning will represent a novelty at international level.