Rapid identification of adulterated cow milk by non-linear pattern recognition methods based on near infrared spectroscopy

Food Chemistry

Volumn 145, Issue , 2014, Pages 342-348

  Zhang, Li Guo ^a   Zhang, Xin ^b   Ni, Li Jun ^a   Xue, Zhi Bin ^a   Gu, Xin ^b   Huang, Shi Xin ^b  

^a EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b Shanghai Municipal Control Institute of Veterinary Drug and Feedstuff   (China)

Author keywords

Improved and simplified; Improved support vector machine; K nearest neighbours; Near infrared spectroscopy Uniform design; Rapid identification of adulterated cow milks

Indexed keywords

CLASSIFICATION MODELS; COW MILK; IMPROVED AND SIMPLIFIED; K-NEAREST NEIGHBOURS; NEAR INFRARED SPECTRA; RAPID IDENTIFICATION; SUPERVISED PATTERN RECOGNITION; UNIFORM DESIGN;

INFRARED DEVICES; NEAR INFRARED SPECTROSCOPY; PATTERN RECOGNITION;

SUPPORT VECTOR MACHINES;

AMMONIUM NITRATE; MELAMINE; UREA;

AQUEOUS SOLUTION; ARTICLE; CALIBRATION; CLASSIFICATION; K NEAREST NEIGHBOR; MILK; MILK PRODUCTION; NEAR INFRARED SPECTROSCOPY; PATTERN RECOGNITION; SUPPORT VECTOR MACHINE; ANALYSIS; ANIMAL; BOVINE; CHEMISTRY; FEMALE; FOOD CONTAMINATION; PROCEDURES;

ANIMALS; CATTLE; FEMALE; FOOD CONTAMINATION; MILK; SPECTROSCOPY, NEAR-INFRARED; SUPPORT VECTOR MACHINES;

EID: 84883620693     PISSN: 03088146     EISSN: 18737072     Source Type: Journal    
DOI: 10.1016/j.foodchem.2013.08.064     Document Type: Article

References (34)

1. R.M. Balabin, and S.V. Smirnov Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: A quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder Talanta 85 2011 562 568
2. L.A. Berrueta, R.M. Alonso-Salces, and K.J. Héberger Supervised pattern recognition in food analysis Journal of Chromatography A 1158 2007 196 214 (Pubitemid 47021153)
3. E. Bertran, M. Blanco, S. Maspoch, M.C. Ortiz, M.S. Sánchez, and L.A. sarabia Handling intrinsic non-linearity in near-infrared reflectance spectroscopy Chemometrics and Intelligent Laboratory Systems 49 2000 215 224
4. A. Borin, M. Ferrao, C. Mello, D. Maretto, and R. Poppi Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk Analytica Chimica Acta 579 2006 25 32 (Pubitemid 44430191)
5. E.Y.Y. Chan, S.M. Griffiths, and C.W. Chan Public-health risks of melamine in milk products The Lancet 372 2008 1444 1445
6. Q.S. Chen, J.W. Zhao, C.H. Fang, and D.M. Wang Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM) Spectrochimica Acta Part A 66 2007 568 574 (Pubitemid 46199298)
7. C. Cortes, and V. Vapnik Support-vector networks Machine Learning 20 1995 273 297
8. P.J. de Groot, G.J. Postma, W.J. Melssen, and L.M.C. Buydens Selecting a representative training set for the classification of demolition waste using remote NIR sensing Analytica Chimica Acta 392 1999 67 75 (Pubitemid 29272009)
9. Y.W. Dong, Z.H. Tu, D.Z. Zhu, Y.W. Liu, Y.N. Wang, and J.L. Huang Feasibility of using NIR spectroscopy to detect melamine in milk Spectroscopy and Spectral Analysis (China) 29 2009 2934 2938
10. G. Downey, V. Fouratier, and J.D. Kelly Detection of honey adulteration by addition of fructose and glucose using near infrared transflectance spectroscopy Journal of Near Infrared Spectroscopy 11 2003 447 456 (Pubitemid 38526498)
11. G. Downey, P. McIntyre, and A.N. Davies Detecting and quantifying sunflower oil adulteration in extra virgin olive oils from the eastern mediterranean by visible and near-infrared spectroscopy Journal of Agricultural and Food Chemistry 50 2002 5520 5552
12. K.T. Fang, D.K.J. Lin, P. Winker, and Y. Zhang Uniform design: Theory and application Technometrics 42 2000 237 248
13. http://news.sohu.com/s2008/babyshenjieshi/, accessed on 04. 27.13.
14. M.H. Hu Neimenggu statistical yearbook 2012 2012 Chinese Statistics Press Beijing
15. Y.H. Lai, Y.N. Ni, and S. Kokot Discrimination of Rhizoma Corydalis from two sources by near-infrared spectroscopy supported by the wavelet transform and least-squares support vector machine methods Vibrational Spectroscopy 56 2011 154 160
16. L. Li, and W. Ding Discriminant analysis of raw milk adulterated with botanical filling material using near infrared spectroscopy Spectroscopy and Spectral Analysis (China) 30 2010 1238 1242
17. Y.Z. Liang, K.T. Fang, and Q.S. Xu Uniform design and its applications in chemistry and chemical engineering Chemometrics and Intelligent Laboratory Systems 58 2001 43 57
18. M. Lin A review of traditional and novel detection techniques for melamine and its analogues in foods and animal feed Frontiers of Chemical Engineering in China 3 2009 427 435
19. L.J. Mauer, A.A. Chernyshova, A. Hiatt, A. Deeding, and R. Davis Melamine detection in infant formula powder using near- and mid-infrared spectroscopy Journal of Agricultural and Food Chemistry 57 2009 3974 3980
20. L.J. Ni, L.G. Zhang, J. Xie, and J.Q. Luo Pattern recognition of Chinese flue-cured tobaccos by an improved and simplified K-nearest neighbors classification algorithm on near infrared spectra Analytica Chimica Acta 633 2009 43 50
21. Ni, L. J., Huang, S. H., Guo, M. L., Zhang, X., Zhang, C., Zhang, L. G, & Wang, X. (2009). Rapid identification of adulteration milk by near infrared spectroscopy. In Proceedings of 14th international conference of near infrared spectroscopy, P182, November 8-13, 2009. Bangkok, Thailand.
22. G. Puchwein Selection of calibration samples for near-infrared spectrometry by factor analysis of spectra Analytical Chemistry 60 1988 569 573
23. P.M.D. Santos, P.D. Wentzell, and E.R. Pereia-Filho Scanner digital images combined with color parameters: A case study to detect adulterations in liquid cow's milk Food Analytical Methods 5 2012 89 95
24. P.M.D. Santos, and E.R. Pereia-Filho Digital image analysis-an alternative tool for monitoring milk authenticity Analytical Methods (Print) 2013 10.1039/ C3AY40561C
25. P.M. Santos, E.R. Pereira-Filho, and L.E. Rodrigues-Saona Rapid detection and quantification of milk adulteration using infrared microspectroscopy and chemometrics analysis Food Chemistry 138 2013 19 24
26. P.M. Santos, E.R. Pereira-Filho, and L.E. Rodrigues-Saona Application of handheld and portable infrared spectrometers in bovine milk analysis Journal of Agricultural and Food Chemistry 61 2013 1205 1211
27. B. Schölkopf, A.J. Smola, R.C. Williamson, and P.L. Bartlett New support vector algorithms Neural Computation 12 2000 1207 1245
28. U. Thissena, M. Pepersb, B. Üstüna, W.J. Melssena, and L.M.C. Buydens Comparing support vector machines to PLS for spectral regression applications Chemometrics and Intelligent Laboratory Systems 73 2004 169 179 (Pubitemid 39265331)
29. V. Vapnik, and J. Wiley Statistical learning theory 1998 John Wiley & Sons Inc New York
30. P.D. Wentzell, and L.V. Montoto Comparison of principal components regression and partial least squares regression through generic simulations of complex mixtures Chemometrics and Intelligent Laboratory Systems 65 2003 257 279 (Pubitemid 36173591)
31. K. Yu, and Y. Cheng Discriminating the genuineness of Chinese medicines using least squares support vector machines Chinese Journal of Analytical Chemistry 34 2006 561 564 (Pubitemid 44428367)
32. S.L. Yuan, Y. He, T.Y. Ma, D. Wu, and P.C. Nie Fast determination of melamine content in milk base on Vis/NIR spectroscopy method Spectroscopy and Spectral Analysis (China) 29 2009 2939 2942
33. Z.Z. Zhong, L.G. Zhang, X. Zhang, X. Gu, and L.J. Ni The feasibility study of discriminating adulteration raw milk by near infrared technology Computers and Applied Chemistry (China) 27 2010 1691 1693
34. D.Z. Zhou, B.P. Ji, C.Y. Meng, B.L. Shi, Z.H. Tu, and Z.S. Qing The performance of ν-support vector regression on determination of soluble solids content of apple by acousto-optic tunable filter near infrared spectroscopy Analytica Chimica Acta 598 2007 227 234