A Discriminant Identification Method of Cheddar Cheese Identity Based on Hierarchical Clustering Extraction Features

Abstract

The invention discloses a method for identifying and identifying cheddar cheese based on features extracted by hierarchical clustering. The characteristic flavor components were hierarchically clustered to generate an identification characteristic flavor component model; the SVM model was used to discriminate the level of unknown cheddar cheese. Using the technical scheme of the present invention to identify the identity of the cheddar cheese can save a lot of labor costs, improve the accuracy of the identity discrimination of the cheddar cheese, and make the cheese classification more objective and effective.

Description

technical field [0001] The invention relates to the identification technology of cheese identity features, in particular to a method for distinguishing identity of cheddar cheese based on hierarchical clustering extraction features. Background technique [0002] Cheddar cheese has a strong milk flavor and fresh nutty flavor, and is currently one of the most imported cheese varieties in my country. According to the flavor, structure, texture, color and appearance of cheddar cheese when mature, cheddar cheese is divided into mild (mild) ), medium, and mature. The longer the ripening time, the better the quality, and thus the higher the price. Generally speaking, the evaluation of different grades of cheese is mostly based on subjective sensory evaluation, and lacks objective evaluation standards. Therefore, the quality of cheddar cheese from different manufacturers and production areas varies greatly. In addition, for our country, which basically relies on imports of cheese, t...

AI Technical Summary

Problems solved by technology

[0003] The flavor of cheese has a great correlation with its quality, which is the key factor affecting its quality. Different grades of cheese have different concentrations of volatile characteristic components. However, most of them use solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS) to obtain the peak areas of volatile components in different cheeses, combined with electronic nose, principal component analysis (Principal Component Analysis), clustering Cluster Analysis, Partial Least Squares and other methods directly identify samples, but do not analyze the unique identity and flavor components of different grades of cheese, and the information features are not enough, so it is impossible to accurately distinguish between cheeses of different maturity stages. cheddar cheese

Images