Infrared spectroscopy tea quality identification method mixed with GK clustering

Abstract

The invention discloses an infrared spectroscopy tea quality identification method mixed with GK clustering in a tea detection technology. A linear discriminant analysis method is used to learn a compressed training sample to acquire a training sample with identification information and a test sample with identification information. Fuzzy C average value clustering is carried out on the training sample with identification information to acquire the initial fuzzy membership degree and an initial clustering center. The fuzzy scattering matrix and the fuzzy membership degree value are calculated, and then a typical value is calculated. The clustering center is calculated according to the typical value. The Euclidean distance from the average value of the training sample with identification information to the clustering center of the test sample is calculated. If the Euclidean distance from the clustering center to the average value of training tea is the minimum, the tea variety of the clustering center and the tea variety of the training sample are the same. The tea and the category of the test sample are determined according to the fuzzy membership degree value. According to the invention, the typical value is added into a function, which can significantly reduce the probability of noise data processing errors.

Description

technical field [0001] The invention relates to tea detection technology, in particular to a tea quality identification method based on GK clustering and infrared spectrum technology. Background technique [0002] In the detection of tea, infrared spectroscopy is a rapid non-destructive detection and analysis technology. Mid-infrared spectroscopy is commonly used to detect tea. The wave number range of mid-infrared spectrum is 4000cm -1 ~400cm -1 The fundamental frequencies of the chemical bond vibrations of most inorganic compounds and organic compounds are in this region. Different functional groups in molecules, types of compounds and three-dimensional structures of compounds have different infrared absorption spectra. Mid-infrared spectroscopy has become an effective detection technology for food and drug detection due to its convenience, speed, efficiency, non-destructiveness, and low cost. [0003] There are two common clustering methods: hard clustering method and ...

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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the existing GK clustering method is prone to errors in the noise data when clustering the infrared spectrum of tea leaves, and proposes a hybrid GK clustering infrared method that is improved and optimized on the basis of the GK clustering method. The spectral tea quality identification method can well cluster the noise-containing tea mid-infrared spectral data and improve the accuracy of tea quality identification

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