FOOD SCIENCE ›› 2020, Vol. 41 ›› Issue (2): 298-305.doi: 10.7506/spkx1002-6630-20190109-108

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Potential Assessment of Stable Isotopes and Mineral Elements for Geographic Origin Identification of Eriocheir sinensis

LUO Renjun, JIANG Tao, CHEN Xiubao, XUE Junren, LIU Hongbo, YANG Jian   

  1. (1. Wuxi Fisheries College, Nanjing Agricultural University, Wuxi 214081, China; 2. Key Laboratory of Fishery Ecological Environment Assessment and Resource Conservation in Middle and Lower Reaches of the Yangtze River, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi 214081, China)
  • Online:2020-01-25 Published:2020-01-19

Abstract: In the present study, chemometrics profiles of mineral elements and stable isotopes for Chinese mitten crabs (Eriocheir sinensis) from three geographic origins in China: the Yellow River (one location) and Liaohe River (two locations) were analyzed and compared with each other to assess the potential of the mineral elements and stable isotopes for geographic origin identification. The results showed that significant (P < 0.05) or highly significant (P < 0.01) differences in the contents of 7 elements (Na, Mg, Al, K, Zn, Sr, and Ba) and two stable isotopes (δ13C and δ15N) in crabs were found among locations, despite no significant differences (P > 0.05) in the contents of Ca, Mn and Cu. Nevertheless, the self-organizing map analysis of the mineral elements and stable isotope profiles visually clustered the two locations of Liaohe River into one group, which was distinguished from the remaining group of crabs from Dongying, located on the estuary of the Yellow River. Linear discriminant analysis showed that the accuracies of initial verification with the mineral elements alone and in combination with the stable isotopes were both higher than 95%, and, moreover, the combination of mineral elements and stable isotopes had an even higher potential for geographic origin identification.

Key words: Eriocheir sinensis, stable isotopes, mineral elements, self-organizing map, linear discriminant analysis, geographic origin discrimination

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