Identification method for producing area of Wuyi rock tea and with deep learning function
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A deep learning and functional technology, applied in chemical machine learning, scientific instruments, chemical statistics, etc., can solve problems such as unable to represent origin traceability
Inactive Publication Date: 2017-04-12
CHINA JILIANG UNIV
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[0012] The purpose of the present invention is to solve the problem that a single detection data cannot represent all the key information of origin traceability and the joint use of different types of detection data in metrology methods, and analyze the existing data matching, etc., to provide Wuyi rock tea origin with deep learning function Identification method, combined with near-infrared spectrum, stable isotope, trace elements and electronic tongue data to establish a Wuyi rock tea origin identification model technology method. The near-infrared characteristic spectrum data, stable isotope data, trace element data and electronic tongue data are integrated to establish an analysis model, and the model is used to objectively and accurately determine the origin of rock tea after extracting samples
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Embodiment 1
[0074] A. Collect rock tea samples from different origins
[0075] 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 marked), a total of 33 sampling points, the sampling range basically covers the main production areas, each sampling point sampling 15 copies (respectively marked with A-1, A-2...A-15), obtained 495 Wuyi rock tea samples from the Geographical Indication Protection Area, and other counties and cities in Fujian Province except Wuyishan City (Jianyang, Jia...
Embodiment 2
[0127] Adopt the same modeling method as embodiment 1, use Kenstone 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 remains unchanged, and stable isotopes, trace elements, and electronic tongues are spliced according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, Sr, Ba, ZZ, BA, BB, CA, GA, HA, and JB, respectively After near-infrared data, its model recognition rate is 94.2%, 87.4%, 88.1%, respectively.
Embodiment 3
[0129] Adopt the same modeling method as embodiment 1, use Kenstone 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 remains unchanged, and the stable isotopes, trace elements, and electronic tongues are spliced in the near-infrared data according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, ZZ, BA, BB, CA, GA, HA, and JB, respectively. After that, the model recognition rates were 98.1%, 88.6%, and 89.7%, respectively.
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Abstract
The invention relates to an identification method for the producing area of Wuyi rock tea and with a deep learning function, belonging to the technical field of identification of the authenticity of products of geographical indication. The objective of the invention is to overcome the problems that single detection data cannot represent all the key information in tracing of the producing area and that data matching is hard to realize for combined usage of detection data of different types in metrological method and other problems in the prior art. According to the invention, near-infrared characteristic spectroscopic data, stable isotope mass spectrometric data, trace element data and electronic tongue data of rock tea from different producing areas (including rock tea from and not from a geographically indicated producing area) are fused together for modeling analysis on the basis of an ELM neural network discrimination model, and the model is used for objectively and accurately determining the producing area of an extracted sample; the identification method has the highest identification rate, as high as 100.0% and higher than the ELM determination results of single data; and the identification method has a blind sample identification rate of 100%, so the method has good application prospects and is applicable as a technical identification method for tracing the producing area of the Wuyi rock tea.
Description
[0001] (1) Technical field [0002] The invention relates to a method for identifying the origin of Wuyi rock tea with a deep learning function, that is, a method for identifying the origin of Wuyi rock tea by combining near-infrared spectroscopy, stable isotopes, trace elements and electronic tongue data, and belongs to the technical field of authenticity identification of geographical indication products. [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. Th...
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