Honey is a natural food sweetener made by bees (Apis mellifera) that contains an important number of bioactive substances, such as polyphenols, which provide health benefits, being therefore highly appreciated by society. These special characteristics, together with the great variability of products due to its worldwide production, have placed honey as one of the products most susceptible to manipulation for illicit purposes, with adulteration with sugars or botanical and geographical origin mislabeling being the most common fraudulent practices. The development of feasible analytical methodologies to assess honey authenticity is therefore required.

In this work, a non-targeted liquid chromatography coupled with mass spectrometry (LC-MS) fingerprinting methodology by employing a hybrid triple–quadrupole/linear ion trap mass analyzer in negative ESI mode, was evaluated to assess honey's geographical origin. For that purpose, one-hundred sixty-nine honey samples produced in eleven countries belonging to four continents (Europe, Asia, America, and Oceania) were analyzed in full scan acquisition mode by registering fingerprints from m/z 100 to 550. Sample treatment consisted of dissolving 1 g of the sample in 10 mL of water, and a 1:1 dilution with methanol prior to LC-MS analysis. The potential of the obtained honey LC-MS fingerprints as sample chemical descriptors for geographical origin authentication was evaluated by supervised partial least squares-discriminant analysis (PLS-DA), revealing good sensitivity and specificity values, as well as very acceptable calibration and prediction classification errors (below 25% for most of the sample classes) when employing a classification decision tree, using 70% of the samples as a calibration set and the other 30% as a prediction set. Taking into consideration the number of analyzed samples (and countries of origin under study), as well as the huge botanical variety and complexity among the analyzed samples, the proposed LC-MS fingerprinting methodology exhibited exceptional performance to assess honey's geographical origin and to fight against fraudulent mislabeling practices.