Acacia honey authenticity identification method based on feature selection and machine learning algorithm

Abstract

The invention discloses an acacia honey authenticity identification method based on feature selection and a machine learning algorithm. The acacia honey authenticity identification method comprises the following steps: collecting true and false honey samples and generating acacia honey data; performing true and false labeling on the acacia honey data to obtain an acacia honey data set; obtaining a low-dimensional acacia honey data set through feature selection; constructing a honey true and false identification model RF-XGBoost; performing parameter optimization and model verification on the model; and carrying out authenticity identification on to-be-detected honey by utilizing the trained model. According to the method, the authenticity of the black locust honey can be effectively and accurately identified, errors caused by manual checking of a spectrogram for authenticity identification are avoided, the accuracy, the root mean square error and the AUC value of the authenticity identification of the black locust honey are effectively improved, the data feature dimension, the model training time, the model complexity and the over-fitting risk are reduced, and the method is an effective method for identifying authenticity of acacia honey.

Description

technical field [0001] The invention relates to a honey authentication technology, in particular to a method RF-XGBoost for authenticity identification of acacia honey based on feature selection and machine learning algorithms. Background technique [0002] Honey is a natural sweet substance that bees collect nectar from the flowers of flowering plants and fully brew in the hive. It has a strong smell and a pure and sweet taste. Honey is a sugar-based natural food, with glucose and fructose as its main components, which can be directly absorbed by the human body without enzymatic decomposition. It is also one of the most commonly used tonics and is deeply loved by consumers. [0003] However, the composition of honey is complex, and the composition content is related to the type of nectar source plant, flowering period, climate, storage and processing technology, etc. At present, the domestic food safety national standard GB / T 18932.1-2002 and GB / T 18932.2-2002, using targ...

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Problems solved by technology

[0003] However, the composition of honey is complex, and the composition content is related to the type of nectar source plant, flowering period, climate, storage and processing technology, etc. At present, the domestic food safety national standard GB / T 18932.1-2002 and GB / T 18932.2-2002, using targeted detection technology, such as mass spectrometry or chromatography technology, to determine the characteristic components of honey blends, however, counterfeiters systematically evade the detection items in the standard National standards can no longer meet the current detection of honey adulteration or mixing, and it is urgent to develop and establish an effective method for authenticating honey based on machine learning

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