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.
摘要：
食品的受保护原产地标记（PDO）旨在保证欧盟内区域产品的生产条件和地理原产地。蜂蜜产品由于其促进健康的特性而被广泛消费，并且对追踪其真实性有着普遍的兴趣。在这方面,宏基因组学测序和机器学习（ML）已被提出作为补充技术，以改善食品的可追溯性方法。因此,这项研究的目的是分析来自三个不同PDO的西班牙蜂蜜的宏基因组概况(格拉纳达，TenerifeandVilluercas-Ibore),并使用ML模型将它们与非PDO蜂蜜进行比较(PLS，射频,LOGITBOOST,和NNET）。根据获得的结果，非PDO蜂蜜和格拉纳达PDO的β多样性值高于特内里费岛和Villuercas-IboresPDO。蜂蜜产品的ML分类允许鉴定蜂蜜地理来源的不同微生物生物标志物：昆基乳杆菌，PDO蜂蜜和类芽孢杆菌幼虫的副食性芽孢杆菌和乳杆菌，非PDO蜂蜜的阿匹诺乳杆菌和肺炎克雷伯菌。此外,一些蜂蜜品种的潜在微生物生物标志物，包括Albaida和RetamadelTeide品种的Kunkeei，塔吉纳斯特品种的青霉，已确定。ML模型在一组独立的样本上进行了验证，从而获得了高准确率(90%以上)。这项工作证明了ML使用基于宏基因组的方法区分不同类型的蜂蜜的潜力，导致高性能指标。此外,ML模型区分与不同PDO和非PDO产品相对应的产品的地理来源和种类。此处介绍的结果可能有助于开发可应用于各种食品的增强的可追溯性和真实性方法。
