Application of next generation semiconductor based sequencing to detect the botanical composition of monofloral, polyfloral and honeydew honey

Honey is one of the most frauded food products. Therefore, it is important to develop new analytical systems useful for its authentication. Honey contains intrinsic markers that can be used to identify and monitor its origin, including plant DNA mainly derived by pollen. In this study, we applied a next generation sequencing approach for honey authentication by detecting the prevalent botanical contribution and botanical composition of honeys of different origin. DNA was isolated from nine honeys (six monofloral honeys produced in Italy, two polyfloral honeys produced in East Europe and Chile respectively, and one honeydew honey) and PCR amplified for a chloroplast trnL barcoding fragment. Obtained amplicons were sequenced using the Ion Torrent sequencing platform. Sequence data was interpreted using a customized bioinformatic pipeline that used a reference plant sequence dataset derived by more than 150,000 entries. A total of 254 botanical groups were identified from the nine analysed samples, ranging from 37 groups in orange tree blossom honey to 74 in eucalyptus tree blossom honey. The prevalent expected botanical origin was confirmed in five out of six monofloral honeys. The plant signature of the labelled lime tree blossom honey did not confirm the expected botanical prevalence. The most represented botanical group in the honeydew honey was Castanea. The botanical composition of monofloral and polyfloral honey samples was useful to infer their geographical origin. The metabarcoding based system applied in this study captured the botanical signature of all analysed honey samples and provided information useful for their authentication.