Differential Feature Extraction Method of Taste Sensing Signals Based on Kernel Linear Discriminant Analysis

Abstract

The present invention provides a method for extracting the difference feature of taste sensory signals based on nuclear linear discriminant analysis. The method includes: using an electronic tongue to detect tea samples to obtain sensor response time series signals; according to the response time series signals, using principal component residuals and The Mahalanobis distance method is used to analyze and eliminate abnormal samples; the parameters of the kernel linear discriminant analysis method are optimized, and the parameters of the kernel linear discriminant analysis method are selected based on the correct recognition rate of the quality grade of Longjing tea; The response signal is extracted with nonlinear features to obtain the taste characteristics of the tea samples; the taste characteristics of the tea samples are input into the classifier to judge the tea quality grade. Remove outliers from tea samples, and use the kernel linear discriminant analysis method after optimizing parameters to better characterize the nonlinear characteristics of tea samples of different grades, and improve the signal difference of samples after nonlinear mapping in high-dimensional feature space .

Description

technical field [0001] The invention relates to the technical field of tea detection, in particular to a method for extracting differences in taste sensory signals based on kernel linear discriminant analysis. Background technique [0002] In recent years, tea quality testing has been a difficult task because tea contains many components and their effects on tea quality are very different. West Lake Longjing tea is a typical representative of Chinese green tea. In order to seek their own interests, some vendors fry other green teas into flat shapes to pass off as Longjing tea, or use Longjing from other places in Zhejiang as West Lake Longjing, which disrupts the Longjing tea market and damages the interests of consumers. Scientific testing and evaluation are of great significance. [0003] For a long time, sensory evaluation has been an important method to evaluate the quality of tea, but this method requires rich knowledge of tea science and evaluation experience, and th...

AI Technical Summary

Problems solved by technology

However, traditional linear feature extraction methods cannot effectively explore the inherent laws existing in nonlinear data.

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