Acta Photonica Sinica, Volume. 42, Issue 9, 1123(2013)
Discrimination of Adulterated Milk Using Least Square Support Vector Machines Combined with Two-dimensional Correlation Infrared Spectroscopy
A new method for the discrimination of adulterated milk based on two-dimensional(2D) correlation infrared spectroscopy and least square support vector machines (LS-SVM) was proposed. 48 pure milk samples were collected and 16 urea-tainted milk (0.01~0.3 g/L), 16 melamine-tainted milk (0.01~0.3 g/L), 16 tetracycline-tainted milk (0.01~0.3 g/L) were prepared. Based on the characteristics of 2D correlation infrared spectra of pure milk and adulterated milk, 6 apparent statistic parameters of all samples were extracted and calculated. These 6 parameters were used as input for LS-SVM to build discriminant model of adulterated milk and pure milk. The recognition rate of unknown samples was 90.6%. The results reveal that parameterization of 2D correlation spectra in combination with LS-SVM method has a feasible potential to discrimination adulterated milk and pure milk.
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YANG Yan-rong, YANG Ren-jie, ZHANG Zhi-yong, YANG Shi-chun, LIANG Peng. Discrimination of Adulterated Milk Using Least Square Support Vector Machines Combined with Two-dimensional Correlation Infrared Spectroscopy[J]. Acta Photonica Sinica, 2013, 42(9): 1123
Received: Apr. 7, 2013
Accepted: --
Published Online: Dec. 18, 2013
The Author Email: Yan-rong YANG (tjshyyr@sina.com)