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Wuyi rock tea production place deep studying system based on five-hiding layer

A technology of deep learning and hidden layers, applied in scientific instruments, material analysis through optical means, measuring devices, etc., can solve problems such as inability to represent the source of origin

Inactive Publication Date: 2017-04-12
CHINA JILIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0014] The purpose of the present invention is to solve the problem that a single type of detection data cannot represent all the key information of origin traceability and the data matching of different types of detection data used in combination in metrology methods, and provide a fusion of near-infrared spectroscopy, stable isotopes, trace elements, pediatric Wuyi rock tea origin identification model technology method based on tea element and electronic tongue data. This method is based on a neural network model with deep learning function, and uses near-infrared characteristic spectral data , stable isotope data, trace element data, catechin and electronic tongue data are fused in the same data table, an analysis model is established, and the model is used to objectively and accurately determine the origin of rock tea after extracting samples

Method used

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  • Wuyi rock tea production place deep studying system based on five-hiding layer
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  • Wuyi rock tea production place deep studying system based on five-hiding layer

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Embodiment 1

[0082] A. Collect rock tea samples from different origins

[0083] The national standard (GB / T 18745-2006) stipulates the scope of geographical protection of Wuyi rock tea, that is, within the administrative division of Wuyishan City, Fujian Province, the present invention is located in Wuyi Street, Chong'an Street, Shangmei, and Xingxia in the Wuyi Rock Tea Geographical Indication Protection Area. Samples were collected in 11 administrative areas including Village, Wufu, Langu, Xinfeng Street, Yangzhuang, Xingtian, Xiamei, and Wutun, and 3 sampling points were randomly selected in each administrative area (respectively A, B, C to be marked), a total of 33 sampling points, the sampling range basically covers the main production areas, and each sampling point takes 15 samples (respectively marked with A-1, A-2...A-15), and obtained 495 samples of Wuyi rock tea in geographical indication protected areas, and other counties and cities in Fujian Province except Wuyishan City (Jian...

Embodiment 2

[0144] Adopt the same modeling method as embodiment 1, use Duplex segmentation program for data segmentation, use K-fold interactive verification, set up neural network ELM, partial least squares PLSDA and least squares support vector machine LS-SVM model respectively, near infrared The data remain unchanged, stable isotopes, trace elements, catechins and e-tongues are classified according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, Sr, Ba, EGC, C, EGCG, GA, EC, After splicing ECG, caffeine, ZZ, BA, BB, CA, GA, HA, and JB into near-infrared data, the model recognition rates were 90.7%, 85.8%, and 86.9%, respectively.

Embodiment 3

[0146] Adopt the same modeling method as embodiment 1, use Duplex segmentation program for data segmentation, use K-fold interactive verification, set up neural network ELM, partial least squares PLSDA and least squares support vector machine LS-SVM model respectively, near infrared The data remain unchanged, stable isotopes, trace elements, catechins and e-tongues are classified according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, Sr, Ba, EGC, C, EGCG, GA, EC, After splicing ZZ, BA, BB, CA, GA, HA, and JB into near-infrared data, the model recognition rates are 96.5%, 87.4%, and 89.1%, respectively.

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Abstract

The invention relates to a Wuyi rock tea production place deep studying system based on five-hiding layer, and belongs to the technical field of geographical indication product authenticity recognition. In the prior art, the single detection data cannot represent all production place traceability key information, the data matching problem exists when different types of the detection data are subjected to combined use in the metrology method, and other problems exist. A purpose of the present invention is to solve the problems in the prior art. According to the present invention, based on the neural network ELM model, the near infrared characteristic spectrum data, the stable isotope data, the trace element data, the catechin data and the electronic tongue data of the rock teas (produced inside and outside the geographical indication production place) from different production places are integrally fused, the analysis model is established, the sample is extracted, and the rock tea production place is objectively and accurately determined by using the model, wherein the recognition rate is highest, achieves 100.0%, and is higher than the ELM result of the single data, and the recognition rate of the blind sample achieves 100%; and the method has the good application prospect, and can be used as the Wuyi rock tea production place traceability recognition technical method.

Description

[0001] (1) Technical field [0002] The present invention relates to a deep learning system for the origin of Wuyi rock tea based on five hidden layers. The five hidden layers include five types of data, namely, near-infrared spectrum, stable isotope, trace element, catechin and electronic tongue, which belong to the authenticity recognition of geographical indication products. technology field. [0003] (2) Background technology [0004] According to the definition of GB / T 17924-2008, a geographical indication product refers to the use of raw materials produced in a specific region and produced in a specific region according to traditional techniques. The quality, characteristics or reputation are essentially determined by the geographical characteristics of the region of origin , and products named after the name of the region of origin have been reviewed and approved according to legal procedures. Tea is a typical product protected by geographical indications. There are nea...

Claims

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Application Information

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IPC IPC(8): G01N21/359G01N21/3563G01N27/62G01N21/31G01N30/02G01N27/00
CPCG01N21/3103G01N21/3563G01N21/359G01N27/00G01N27/62G01N30/02
Inventor 付贤树叶子弘俞晓平崔海峰张雅芬
Owner CHINA JILIANG UNIV
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