Intelligent identification method for producing area of Wuyi rock tea based on multiple inspection techniques
A technology of origin and rock tea, applied in measuring devices, material analysis by optical means, instruments, etc., can solve the problem that the detection data cannot represent the key information of origin traceability and so on.
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Embodiment 1
[0088] A. Collect rock tea samples from different origins
[0089] 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
[0166] Adopt the modeling method identical with embodiment 1, data segmentation uses Kenstone segmentation program, with K-fold interactive verification, respectively establishes PLSDA, neural network ELM and least squares support vector machine LS-SVM model, near-infrared data is constant, Stable isotopes, trace elements, amino acids, catechins, and e-tongues are classified according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, Sr, Ba, asparagine, proline, tryptophan, Phosphoethanolamine, urea, valine, EGC, C, EGCG, GA, EC, ECG, caffeine, ZZ, BA, BB, CA, GA, HA, and JB were stitched together in near-infrared data, and their model recognition rates were 89.5% %, 83.2%, 87.7%.
Embodiment 3
[0168] Adopt the modeling method identical with embodiment 1, data segmentation uses Kenstone segmentation program, with K-fold interactive verification, respectively establishes PLSDA, neural network ELM and least squares support vector machine LS-SVM model, near-infrared data is constant, Stable isotopes, trace elements, amino acids, catechins, and e-tongues are classified according to hydrogen, oxygen, nitrogen, carbon, strontium, Cs, Cu, Ca, Rb, Sr, Ba, asparagine, proline, tryptophan, The model recognition rates of phosphoethanolamine, urea, valine, EGC, C, EGCG, GA, EC, ZZ, BA, BB, CA, GA, HA, and JB were 90.1% and 83.8% respectively after splicing the near-infrared data , 88.9%.
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