Method for identifying specific geographical indications of Wuyi rock tea
A geographical indication and rock tea technology, which is applied in the field of authenticity identification of geographical indication products, can solve problems such as unrepresentable detection data and full traceability of origin
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Embodiment 1
[0070] A. Collect rock tea samples from different origins
[0071] 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 of Village, Wufu, Langu, Xinfeng Street, Yangzhuang, Xingtian, Xiamei, and Wutun, and 3 sampling points were randomly selected in each administrative area (in the order of 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, Jianou,...
Embodiment 2
[0126] Using the same modeling method as in Example 1, the data segmentation uses the kenstone segmentation program, and uses the K-fold interactive verification to establish a neural network, partial least squares (PLSDA), and least squares support vector machine (LS-SVM) respectively Models, stable isotopes, trace elements, catechins spliced together according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, Sr, Ba, EGC, C, EGCG, GA, EC, ECG and caffeine , and their model recognition rates are 94.8%, 81.7% and 83.5%, respectively.
Embodiment 3
[0128]Using the same modeling method as in Example 1, the data segmentation uses the kenstone segmentation program, and uses the K-fold interactive verification to establish a neural network, partial least squares (PLSDA), and least squares support vector machine (LS-SVM) respectively Models, stable isotopes, trace elements, and catechins are spliced together according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, Sr, Ba, EGC, C, EGCG, GA, and EC, and the model identification The rates were 95.9%, 82.0% and 85.4%.
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