Wuyi rock tea production place identification method through combination of three detection methods
A detection method and a technology of origin, applied in measuring devices, material analysis through optical means, instruments, etc., can solve problems such as inability to represent origin traceability, and achieve avoidance of insensitive loss functions, high recognition rate, and reduced computational complexity Effect
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Embodiment 1
[0083] A. Collect rock tea samples from different origins
[0084] 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
[0145]Adopt the same modeling method as embodiment 1, use Duplex segmentation program for data segmentation, use Monte Carlo interactive verification, establish least squares support vector machine LS-SVM, partial least squares PLSDA, neural network ELM model respectively, near infrared The data remains unchanged, and the stable isotopes and catechins are spliced in the near-infrared data according to hydrogen, oxygen, nitrogen, carbon, strontium, EGC, C, EGCG, GA, EC, ECG, and caffeine respectively, and the model recognition rates are 94.1% respectively , 90.5%, 91.6%.
Embodiment 3
[0147] Adopt the same modeling method as embodiment 1, use Duplex segmentation program for data segmentation, use Monte Carlo interactive verification, establish least squares support vector machine LS-SVM, partial least squares PLSDA, neural network ELM model respectively, near infrared The data remains unchanged, and the stable isotopes and catechins are spliced in the near-infrared data according to hydrogen, oxygen, nitrogen, carbon, strontium, EGC, C, EGCG, GA, EC, respectively, and the model recognition rates are 97.9%, 92.3%, 93.8%.
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