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Tea infrared spectrum classification method of fuzzy discrimination clustering

A technology of infrared spectrum and classification method, which is applied in the field of tea classification, can solve the problems of unable to dynamically extract identification information, change data dimension, etc., and achieve the effect of improving classification accuracy, high clustering accuracy, and improving accuracy

Inactive Publication Date: 2017-02-15
JIANGSU UNIV
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Problems solved by technology

However, FCM cannot dynamically extract identification information and change the data dimension during the clustering process.

Method used

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  • Tea infrared spectrum classification method of fuzzy discrimination clustering
  • Tea infrared spectrum classification method of fuzzy discrimination clustering
  • Tea infrared spectrum classification method of fuzzy discrimination clustering

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Experimental program
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Effect test

Embodiment

[0049] Take high-quality Leshan Zhuyeqing, low-quality Leshan Zhuyeqing and Emeishan Maofeng tea, and use a spectrometer to collect infrared spectrum samples, such as figure 2 shown. 32 samples were collected for each category of tea, and a total of 96 samples were obtained. Each sample was a 1868-dimensional data, and 22 samples were selected as test samples for each category. There were 66 test samples for the three categories of tea, and the remaining The 30 samples are used as training samples.

[0050] The tea infrared spectrum sample data were preprocessed with multivariate scattering correction, and obtained as follows: image 3 The spectrogram shown. Then use the principal component analysis method to reduce the dimensionality of the sample data: because the cumulative reliability of the first 14 principal components is 100% > 98%, the first 14 eigenvectors v are obtained by decomposing the infrared spectrum of the tea sample. 1 , v 2 …v 14 and the corresponding ...

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Abstract

The invention discloses a tea infrared spectrum classification method of fuzzy discrimination clustering. A linear discrimination analyzing method is employed to extract the identification information of 14-dimensional training sample data, and 14-dimensional test sample data is projected to a discrimination vector to obtain the two-dimensional test sample data. The two-dimensional test sample data are subjected to fuzzy C-means clustering. A fuzzy interclass scattering matrix is calculated according to an initial clustering center, and the fuzzy total scattering matrix is calculated. An eigenvector is calculated according to the fuzzy interclass scattering matrix and the fuzzy total scattering matrix. A clustering central value is calculated in a characteristic space through the fuzzy membership function value. The average value of each 14-dimensional training sample is calculated respectively, and the Euclidean distance of the average values of the clustering central value and the training samples of the test samples. If the Euclidean distance from the clustering central value to the training samples is minimal, the tea belonging to the clustering central value is of the same type with the tea of the training samples, thereby realizing correct classification of different tea types.

Description

technical field [0001] The invention relates to a tea classification method, in particular to a tea infrared spectrum classification method based on fuzzy identification and clustering. Background technique [0002] Tea is one of the most consumed beverages today. With the improvement of living standards, the quality requirements of tea are getting higher and higher, how to choose tea leaves reasonably has been paid more and more attention by people. It is an important task for scientific researchers to study simple, fast and high accuracy identification methods. [0003] Mid-infrared spectroscopy is mainly used for qualitative and quantitative analysis of organic compounds. Its frequency is at 4000cm -1 ~625cm -1 In between, it is the fundamental vibration frequency range of general organic compounds, which can give very rich structural information: the characteristic group frequency in the spectrum indicates the existence of functional groups in the molecule, and the e...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2321G06F18/24
Inventor 武小红翟艳丽武斌田潇瑜孙俊傅海军戴春霞
Owner JIANGSU UNIV
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