Grape wine classification method based on Bayesian optimization and electronic nose

Abstract

The invention relates to a grape wine classification method based on Bayesian optimization and an electronic nose, and the method comprises the following steps: S1, employing a LightGBM algorithm, employing a Leaf-wise tree building method, finding a leaf with the maximum splitting gain from all current leaves each time during tree building, then splitting, and repeating the above steps; the LightGBM uses the maximum tree depth to prune the tree, and excessive fitting is avoided; S2, building a Bayesian optimization algorithm; S3, building a BO-LightGBM, and performing self-optimization adjustment on hyper-parameters of the LightGBM by using a Bayesian hyper-parameter optimization algorithm; enabling bayesian optimization to use a probability model to replace a complex optimization function, introducing the prior of a to-be-optimized target into the probability model, thus the model can effectively reduce unnecessary sampling. The Bayesian optimization method has the advantages that the Bayesian optimization method determines the optimization method of the next evaluation point by constructing the probability model of the function to be optimized and utilizing the probability model, the most advanced result is achieved on some global optimization problems, and the Bayesian optimization method is a better solution for hyper-parameter optimization.

Description

technical field [0001] The invention relates to the technical field of wine intelligent classification, in particular to a wine classification method based on Bayesian optimization and electronic nose. Background technique [0002] Wine, a beverage obtained by fermenting and aging grapes, is one of the most popular beverages in the world. The classification of wine is very important to maintain the high economic value of wine products, protect the quality of wine, prevent illegal labeling, and ensure the quality of imported and exported wine. At present, the commonly used wine detection methods mainly include chemical composition analysis, professional sommelier identification, and gas chromatography. Because these methods are time-consuming, laborious, and inefficient, it is particularly important to study a fast and efficient wine classification method. [0003] As a healthy drink, wine has been welcomed by everyone, and at the same time, more and more attention has been ...

AI Technical Summary

Problems solved by technology

However, the issue of classification models is rarely considered in electronic nose detection

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