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• W3196364539 endingPage "131066" @default.
• W3196364539 startingPage "131066" @default.
• W3196364539 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." @default.
• W3196364539 created "2021-09-13" @default.
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• W3196364539 date "2022-03-01" @default.
• W3196364539 modified "2023-10-01" @default.
• W3196364539 title "Tracing the origin of honey products based on metagenomics and machine learning" @default.
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• W3196364539 doi "https://doi.org/10.1016/j.foodchem.2021.131066" @default.
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