Scientia Agricultura Sinica ›› 2011, Vol. 44 ›› Issue (22): 4653-4659.doi: 10.3864/j.issn.0578-1752.2011.22.012

• STORAGE·FRESH-KEEPING·PROCESSING • Previous Articles     Next Articles

The Origin Discrimination of Tartary Buckwheat Based on the Mineral Elements

 ZHANG  Qiang, LI  Yan-Qin   

  1. 1.山西师范大学生命科学学院，山西临汾 041004
    2.山西大学生物技术研究所/化学生物学与分子工程教育部重点实验室，山西太原 030006
  • Received:2011-04-14 Online:2011-11-15 Published:2011-09-09

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Abstract: 【Objective】The characteristics of mineral elements in tartary buckwheat from different provinces were analyzed in order to make choice of the effective index in tartary buckwheat origin discrimination, and to explore the method of tartary buckwheat origin traceability and discrimination.【Method】Seven mineral element contents (Cu, Zn, Fe, Mn, Ca, P and Se) were analyzed in thirty-nine samples from five provinces of China（i.e., Shanxi, Gansu，Qinghai，Sichuan and Yunnan）. On the basis of stepwise selection，the K Nearest Neighbor (KNN) analysis of nonparametric discriminant was applied to data analysis.【Result】The results showed that the mineral element contents were different in tartary buckwheat varieties from different provinces. In conclusion, element contents of Cu and P were the highest in the Tartary buckwheat varieties from Yunnan while Se was the highest in those from Shanxi. However, the contents of Zn，Fe and Ca were the highest in those from Qinghai, while Cu, Zn, Fe, Ca and P were the least in those from Sichuan, and Mn was the least in those from Gansu. Then, mineral element character indexes (Zn, Mn, Ca, P and Se) affecting the classification greatly were selected with discrimination analysis. A percentage of 97.4% of correct classification was achieved by resubstitution and cross-validated.【Conclusion】The determination of mineral element content in combination with modern statistical techniques should be a useful and convenient tool for the origin discrimination and standardization of tartary buckwheat.

Key words: tartary buckwheat, mineral element, discriminant analysis, K Nearest Neighbor, data mining

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