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A Method of Electronic Tongue Signal Feature Extraction Based on Manifold Learning

A manifold learning and signal feature technology, applied in the field of electronic tongue signal feature extraction based on manifold learning, can solve the problem of unable to extract non-characteristics of tea leaves

Active Publication Date: 2019-01-15
UNIV OF SCI & TECH BEIJING
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Problems solved by technology

However, existing analysis tools cannot perform non-characteristic extraction of different grades of tea

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  • A Method of Electronic Tongue Signal Feature Extraction Based on Manifold Learning
  • A Method of Electronic Tongue Signal Feature Extraction Based on Manifold Learning
  • A Method of Electronic Tongue Signal Feature Extraction Based on Manifold Learning

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[0063] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0064] like figure 1 As shown, a method for extracting electronic tongue signal features based on manifold learning in an embodiment of the present invention, the method for extracting electronic tongue signal features based on manifold learning includes:

[0065] Step 101: Use the electronic tongue to detect the tea samples, and obtain the sensor response timing signal;

[0066] Step 102: Analyzing and eliminating abnormal samples by using principal component residual and Mahalanobis distance method according to the response time series signal;

[0067] Step 103: optimize the parameters of the manifold learning algorithm, and select the parameters of the manifold learning algorithm based on the correct recognition rate of the quality grade of Longjing ...

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Abstract

The invention provides a feature extraction method for signals of an electronic tongue based on manifold learning. The method comprises the following steps: detecting tea samples with the electronic tongue so as to obtain sensor-responsive sequence signals; analyzing and rejecting abnormal samples by using a principal component residual method and a Mahalanobis distance method; optimizing the parameters of a manifold learning algorithm and selecting the parameters of the manifold learning algorithm on the basis of the correct recognition rata of the quality grade of Longjing tea; carrying out non-linear feature extraction on sensor-responsive signals by using the manifold learning algorithm so as to obtain characteristics characterizing the taste information of the tea samples; and inputting the taste characteristics of the tea samples into a classifier and determining the quality grade of the Longjing tea. The feature extraction method can reject the abnormal values of the tea samples; and the parameter-optimized manifold learning algorithm can better characterize the non-linear characteristics of tea samples of different grades and improve signal difference of samples having undergone nonlinear mapping in high-dimensional feature space.

Description

technical field [0001] The invention relates to the technical field of tea detection, in particular to an electronic tongue signal feature extraction method based on manifold learning. 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 the sensitivity of sensory ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N27/27
CPCG01N27/27
Inventor 支瑞聪张德政
Owner UNIV OF SCI & TECH BEIJING
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