Quantization of Adulteration Ratio of Raw Cow Milk by Least Squares Support Vector Machines (LS-SVM) and Visible/Near Infrared Spectroscopy

  • Ching-Lu Hsieh
  • Chao-Yung Hung
  • Ching-Yun Kuo
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 363)


Raw cow milk has short supply market in summer and over supply in winter, which causes consumers and dairy industry concern about the quality of raw milk whether is adulated with reconstituted milk (powdered milk). This study prepared 307 raw cow milk samples with various adulteration ratios 0%, 2%, 5%, 10%, 20%, 30%, 50%, 75%, and 100% of powdered milk. Least square support vector machine (LS-SVM) was applied to calibrate the prediction model for adulteration ratio. Grid search approach was used to find the better value of network parameters of γ and σ 2. Results show that R2 ranges from 0.9662 to 0.9777 for testing data set with plate surface and four concave regions. Scatter plot of testing data showed that adulteration ratio above 10% clearly differs from 0% samples.


LS-SVM Raw milk Cow milk Adulteration detection NIR 


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Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • Ching-Lu Hsieh
    • 1
  • Chao-Yung Hung
    • 1
  • Ching-Yun Kuo
    • 2
  1. 1.Department of Biomechatronics EngineeringNational Pingtung University of Science and TechnologyTaiwan, R.O.C.
  2. 2.Animal Research Institute, Council of Agriculture, Executive YuanTaiwan, R.O.C.

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