Method for tracking honey source of honey by utilizing metagenome and machine learning

Abstract

The invention discloses a method for tracking a honey source of honey by utilizing metagenome and machine learning. According to the method, the problem that molecular databases of plant species are deficient in honey sources, especially remote mountainous areas, is fully considered, and sequence information of nucleic acid substances in honey is analyzed, so that plant sequences contained in honey are obtained; and calculation is performed by a machine learning method to obtain a weight relationship between the plant-derived nucleic acid sequence (without a specific species identification result) in the honey and the honey source, and the honey source of the honey sample is tracked by using the weight relationship as a reference database. The method does not depend on an existing honey source plant molecular database, honey source place information of honey can be directly obtained through honey plant sequence analysis and machine learning, and the method has extremely important value for honey product identification and market standardization.

Description

technical field [0001] The invention relates to the fields of metagenomic sequencing, species diversity analysis, machine learning and bioinformatics, in particular to a method for tracking honey sources by using metagenomic and machine learning. Background technique [0002] In the domestic honey market, most of the nectar produced by Apis mellifera is single-floral honey (single-flower nectar), such as acacia nectar and rape honey, while Chinese honey bees often produce multi-floral honey (hundred-flower nectar). Compared with Apis mellifera, Apis mellifera has a better ability to produce honey, so it was first introduced to China around 1900. And with the introduction of western honeybees, a series of modern industrialized management models have also been introduced, such as the daily cleaning and migration of bee colonies. However, beekeepers in a large number of places still have higher enthusiasm for raising honeybees, especially in remote mountainous areas. Neverthe...

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Problems solved by technology

[0006] The purpose of the present invention is to address the above-mentioned shortage of plant diversity molecular databases, and to provide a method of machine learning to establish weight coefficients between plant sequences and nectar sources, so as to achieve the purpose of tracking the nectar sources of honey samples

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