ABSTRACT
The Protected Designation of Origin (PDO) indication for foods intends to guarantee the conditions of production and the geographical origin of regional products within the European Union. Honey products are widely consumed due to their health-promoting properties and there is a general interest in tracing their authenticity. In this regard, metagenomics sequencing and machine learning (ML) have been proposed as complementary technologies to improve the traceability methods of foods. Therefore, the aim of this study was to analyze the metagenomic profiles of Spanish honeys from three different PDOs (Granada, Tenerife and Villuercas-Ibores), and compare them with non-PDO honeys using ML models (PLS, RF, LOGITBOOST, and NNET). According to the results obtained, non-PDO honeys and Granada PDO showed higher beta diversity values than Tenerife and Villuercas-Ibores PDOs. ML classification of honey products allowed the identification of different microbial biomarkers of the geographical origin of honeys: Lactobacillus kunkeei, Parasaccharibacter apium and Lactobacillus helsingborgensis for PDO honeys and Paenibacillus larvae, Lactobacillus apinorum and Klebsiella pneumoniae for non-PDO honeys. In addition, potential microbial biomarkers of some honey varieties including L. kunkeei for Albaida and Retama del Teide varieties, and P. apium for Tajinaste variety, were identified. ML models were validated on an independent set of samples leading to high accuracy rates (above 90 %). This work demonstrates the potential of ML to differentiate different types of honey using metagenome-based methods, leading to high performance metrics. In addition, ML models discriminate both the geographical origin and variety of products corresponding to different PDOs and non-PDO products. Results here presented may contribute to develop enhanced traceability and authenticity methods that could be applied to a wide range of foods.
