The purpose of this study was the physicochemical characterization and classification of Italian honey from Marche Region with a chemometric approach. A total of 135 honeys of different botanical origins [acacia ( Robinia pseudoacacia L.), chestnut ( Castanea sativa), coriander ( Coriandrum sativum L.), lime ( Tilia spp.), sunflower ( Helianthus annuus L.), Metcalfa honeydew and multifloral honey] were considered. The average results of electrical conductivity (0.14 – 1.45 mS cm−1), pH (3.89 – 5.42), free acidity (10.9 – 39.0 meqNaOH kg−1), lactones (2.4 – 4.5 meqNaOH kg−1), total acidity (14.5 – 40.9 meqNaOH kg−1), proline (229–665 mg kg−1) and 5-(hydroxy-methyl)-2-furaldehyde (0.6–3.9 mg kg−1) content show wide variability among the analysed honey types, with statistically significant differences between the different honey types. Pattern recognition methods such as principal component analysis and discriminant analysis were performed in order to find a relationship between variables and types of honey and to classify honey on the basis of its physicochemical properties. The variables of electrical conductivity, acidity (free, lactones), pH and proline content exhibited higher discriminant power and provided enough information for the classification and distinction of unifloral honey types, but not for the classification of multifloral honey (100% and 85% of samples correctly classified, respectively).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.