Rapid identification of water-injected pork and gum-injected pork by the combination of low field nuclear magnetic resonance and chemometrics analysis
Author:
Affiliation:

Testing Center of Animal, Plant and Food, Nanjing Customs, Jiangsu Nanjing 210019, China

Clc Number:

R155

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Objective To apply rapid identification of water-injected and gum-injected pork by chemometrics analysis combined with low field nuclear magnetic resonance.Methods 17 variables obtained by low field nuclear magnetic resonance were used to extract principal components by principal component analysis, and 4 principal components were identified by discriminate analysis. A three-layer MLP neural network model with a structure of 17-6-3 was established through constrained optimization modeling using 17 original variables as input, and the types of water-injected pork and gum-injected pork as output.Results In the discriminate analysis model, the discriminant accuracy of cross-validation in the control group, water-injected group and gum-injected group was 60/60, 91/95 and 359/384, and the total correct discriminant rate was 94.6%. In the neural network model, the total correct discriminant rate of 179 samples in the testing set was 97.8%.Conclusion Low-field nuclear magnetic resonance combined with discriminate analysis model and neural network model can effectively distinguish water-injected and gum-injected pork. The method is simple and can be used for rapid detection.

    Reference
    Related
    Cited by
Get Citation

XU Ruiping, JI Meiquan, DING Tao, LIU Yun, FEI Xiaoqing. Rapid identification of water-injected pork and gum-injected pork by the combination of low field nuclear magnetic resonance and chemometrics analysis[J].中国食品卫生杂志,2022,34(4):724-729.

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:February 18,2022
  • Revised:
  • Adopted:
  • Online: August 26,2022
  • Published: