The Trace And Authentication Technologies On Plant Derived Agricultural Products

In present study, the geographical origin and the authenticity of plant agricultral products (tea, peanut, hangbaiju, rice and honey) were determined with various technonolgies. And then the problems of safety and geographical origin of food could be solved. And the results were as following:1Near infrared spectroscopy was used to identify different kinds of fermented tea which collected from Anhui, Chongqing, Fujian, Guangdong, Guangxi, Hainan, Hubei, Henan, Sichuan, Shandong, Yunnan and Zhenjiang. It was shown that the classification rates of origin and cross-validation test were100%and94.4%, respectively, in canonical discriminant analysis. In order to discriminate the geographical origin of tea, near infrared spectroscopy and stable isotope technologies were used. It was shown that with the combination of spectroscopy of tea water and tea powder the classification rates of geographical origin of tea were100%and94.6%in origin and cross-validation test respectively; while the stable isotope techonolgy could only discriminate the geographical origin of tea from provinces of Anhui, Zhejiang and the southern China (Fujian, Guangdong, Guangxi and Hainan).2Near infrared spectroscopy was used to identify Xihu Longjing and Zhejiang Longjing using partial least squared regression discriminate analysis (LSSVM), Back propagation neural network (BPNN) and radial basis function neural network (RBFNN). Xihu Longjing and Zhejiang Longjing were discriminated from each other perfectively in LSSVM.3The geographical origin of peanuts, collected from Shandong, Hubei, Henan, Liaoning, Guangdong and Guangxi, Sichuan, was identificated, and the classification rates of origin and cross-validation test were100%and55.9%respectively with the K near neibours methods combined with near infrared spectroscopy technology.4It was shown that the elements of discriminant model of place of origin (POO) were Pb, Sr, Ba, Ga and V, and the original and cross-validated classification rates were100%and97%respectively. The elements of discriminant model of different growth area in Tongxiang were Cd, Pb and Rb and the original and cross-validated classification rates were76.70%and70.0%respectively.5It was shown that tansgenic rice, TCTP and mi166, and Zhonghua11were all well discriminated from each other with NIR and PLS-DA. Heavy metal (mercury, cadmium and lead) polluted leaves of rice were identificated with near infrared spectroscopy. It was also shown that the classification rates of mercury, cadmium and lead polluted and control leaves of rice were95.5%,81.8%,91.3%and100.0%respectively in RBFNN model with the treatment of wavelet transform function db2at3level.6In the experiment of identification of commercial honey, collected form Heilongjiang, Liaoning, Zhejiang, Fujian, Chengdu, Hubei, Sichuan, Guangxi, Guangdong, Shanxi, Xinjiang, Tibet, Chongqing, carbon stable isotope ratio technology was first used to identify the adulteration of honey, and then the near infrared spectroscopy technology was used to predict the aluteration of honey. It was showed that the classification rates of calibration and validation test were100%and93.3%respectively. The standarded model of adulterated honey was constructed with the high fructose corn sugar (HFCS), soft sugar (SC) and honey.