Tracing the origin of honey products based on metagenomics and machine learning. (1st March 2022)

• Record Type: Journal Article
• Authors: Liu, Shanlin; Lang, Dandan; Meng, Guanliang; Hu, Jiahui; Tang, Min; Zhou, Xin
• Is Part Of: Food chemistry. Volume 371(2022)
• Publication Date: 2022-03-01
• Subjects: Honeybee -- Honey adulteration -- Pollen -- Floral composition -- Genomics -- Machine learning -- Resilient backpropagation; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2021.131066
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Pollen diversity helps to trace the geographic origin of honey products. Metagenomic sequences are used directly as references for local honey. Plant identification or floral survey are no longer needed. Machine learning can accurately trace geographic origin at high resolution. Method is useful where biodiversity is used as matrix for similarity evaluation. Abstract: The adulteration of honey is common. Recently, High Throughput Sequencing (HTS)-based metabarcoding method has been applied successfully to pollen/honey identification to determine floral composition that, in turn, can be used to identify the geographical origins of honeys. However, the lack of local references materials posed a serious challenge for HTS-based pollen identification methods. Here, we sampled 28 honey samples from various geographic origins without prior knowledge of local floral information and applied a machine learning method to determine geographical origins. The machine learning method uses a resilient backpropagation algorithm to train a neural network. The results showed that biological components in honey provided characteristic traits that enabled accurate geographic tracing for nearly all honey samples, confidently discriminating honeys to their geographic origin with >99% success rates, including those separated by as little as 39 km.