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Study on Detection Technology of Milk Powder Based on Support Vector Machines and Near Infrared Spectroscopy

  • Jingzhu Wu
  • Shiping Zhu
  • Yun Xu
  • Yiming Wang
Part of the The International Federation for Information Processing book series (IFIPAICT, volume 259)

This paper presents a novel classifier to identify standard and sub-standard milk powder, which is built by support vector machines (SVM) and near infrared spectroscopy (NIR). The training set is composed of 38 samples and the testing set is composed of 12 samples. The correct classification ratio of the training set is up to 100%, while that of the testing set is up to 100%. The result indicates that the combination of SVM and NIR can be used as a fast, convenient, and safe technology to identify standard and sub-standard milk powder.

Keywords

Near Infrared Spectroscopy Support Vector machines Milk Powder 

References

  1. Zhang Xuegong, Introduction to Statistical Learning Theory and Support Vector Machines, Acta Automation Sinica 26 32-42 (2000)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Jingzhu Wu
    • 1
  • Shiping Zhu
    • 2
  • Yun Xu
    • 3
  • Yiming Wang
    • 3
  1. 1.College of Information EngineeringBeijing Technology & Business UniversityChina
  2. 2.College of Engineering and TechnologySouthwest UniversityChina
  3. 3.College of Information and Electrical EngineeringChina Agricultural UniversityChina

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