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Authors
Veleva-Doneva, Petya; Draganova, Tsvetelina; Atanasova, Stefka; Tsenkova, Roumiana

Digital Object Identifier (DOI)
10.3182/20101206-3-JP-3009.00039

Page Numbers:
225-229

Index Terms
milk,mastitis,NIR spectroscopy,SIMCA,neuro-fuzzy

Abstract
Potential of near-infrared spectrocopy combined with multivarite classification methods for detection of bacteria in cow milk was investigated. Spectra of milk samples were obtained in a region from 600 to 1880nm. Presence of Staphylococcus aureus, Streptococcus agalactiae and other bacteria samples by classical microbiological methods. One hundred milk samples negative for bacteria (class healthy) and one hundred milk samples with presence of bacteria (class contaminated) were used in the investigation. Two classification methods - Soft Independent Modeling of Class Analogy (SIMCA) and adaptive Neuro - Fuzzy Inference System (ANFIS) were implemented. SIMCA develops models for each class based on principal components (PC) that describe the variations of the spectral data. One each class has its own model, new samples could be classified to one or another classes according to their spectra. The inputs to ANFIS were several principal components. ANFIS had only one output node, the class type. One-half of samples from each class were used as a training set for creation of SIMCA models or trained the ANFIS. The rest of the samples were used as test set for verification theo btained classifiers. SIMCA models, based on 7 PC correct classified 90% of samples from class contaminated and 88% of samples from class healthy for training data set. The results for testing the models with samples from test set were as follow: 90% of samples from class contaminated and 86% of samples from class healthy were correct classified. The average testing error for ANFIS classifier was 0.058% for class healthy was 0,032% for class contaminated. Results of the presented experiments showed possibility to establish classifiers for identification of raw milk samples, infected with bacteria. Near infrared spectroscopy in combination with multivariate classification techniques offers an alternative appraoch to traditional methods with large potentials for a rapid and reliable application in microbiology, bio-diagnostics and food control.

References

[1] Dayhoff, J.E. (1990). Neural Networks Principles. Prentice-Hall
     Internatioanal. USA.
[2] Farkas, I., Remenyi, P., Biro, A. (2000). A neural network
    topology for modelling grain drying. Computers and
    Electronics in Agriculture, 26, 147-158.
[3] Forina, M., Lanteri, S. (1984). Data Analysis in Food
    Chemistry. In B.R. Kowalski, (ed.), Chemometrics.
    Mathematics and Statistics in Chemistry, D. Reidel
    Publishing Company, 305-349.
[4] Forina, M., Lanteri, S., Casale, M. (2007). Multivariate
    calibration. Journal of Chromatography A, 1158, 61-93.
[5] Hamann, J. and Kroemker, V. (1997). Potential of specific
    milk composition variables for cow health management,
    Livest. Prod. Sci., 48, 201-208.
[6] Harmon, R.J. (1994). Physiology of mastitis and factors
    affecting somatic cell counts, J. Dairy Sci., 77, 2103-
    2112.
[7] Heise, H.M. (2000). Clinical application of near-and mid-infrared
     spectoscopy. In H. Gremlich and B. Yan.
    Marsel Dekker (ed.), Infrared and Raman Spectoscopy
    of Biological Materials, New York, 259-322.
[8] Khanna, T. (1990). Foundations of Neural Networks.
    Addison-Wesley Publishing Company. USA.
[9] Martens, H., Naes, T. (1991). Multivariate Calibration,
    Wiley, Chichester.
[10] Naes, T. (1992). In: K.I. Hildrum, T. Isaksson, T. Naes, A.
     Tandberg (ed.), Near Infrared Spectroscopy. Bridging
     the Gap Between Data Analysis and NIR Applications,
     Ellis Horwood Limited, Chichester, West Sussex, UK,
     51-60.
[11] Naes, T., Isaksson, T., Fearn, T., Davies, A. (2002). A User-friendly
      Guide to Multivariate Calibration and
     Classification, NIR Publications, Chichester, UK.
[12] Naumann, D., Helm, D. and Labischinski, H. (1991).
     Microbial characterizations by FT-IR spectroscopy,
     Nature, 351, 81-82.
[13] Oberreuter, H., Brodbeck, A., von Stetten, S., Goerges, S.,
     Scherer, S. (2003). Fourier-transform infrared (FT-IR)
     spectroscopy is a promising tool for monitoring the
     population dynamics of microorganisms in food stuff.
     Europ. Food Research and Technol., 216, 434-439.
[14] Perez-Marin, D., Garrido-Varo, A., Guerrero, J.E. (2007).
     Non - linear regression methods in NIRS quantitative
     analysis, Talanta, 72, 28-42.
[15] Pyorala, S. (2003). Indicators of inflammation in the
     diagnosis of mastitis, Vet. Res., 34, 565-578.
[16] Reneau, J.K. and Packard, V.S. (1991). Monitoring mastitis,
     milk quality and economic losses in dairy fields, Dairy,
     Food and Environmental Sanitation, 11, 4-11.
[17] Saranwong, S. and Kawano, S. (2008). Interpretation of near
     infrared calibration structure for determinating the total
     aerobic bacteria count of raw milk: interaction between
     bacterial metabolites and water absorptions, J. Near
     Infrared Spectroscopic, 16, 497-504.
[18] Tsenkova, R., Atanassova, S., Kavano, S., and Toyoda, K.
     (2001). Somatic cell count determination in cow's milk
     by near-infrared spectroscopy: A new diagnostic tool, J.
     Anim. Sci., 79, 2550-2557.
[19] Tsenkova, R., Atanassova, S., Morita, H., Ikuta, K., Toyoda,
     K., Iordanova, I., and Hakogi, E. (2006). Near infrared
     spectra of cow's milk for milk quality evaluation: diesease
     diagnosis and pathogen identification, J. Near Infrared
     Spectroscopic, 14, 363-370.
[20] Quinn, P., Carter, M.E., Markey, B.K., and Carter, G.R.
     (1999). Clinical Veterinary Microbiology, Mastitis, 327-
     344.
[21] Wold, S. (1976). Pattern Recognition by Means of Disjoint
     Principal Components Models, Pattern Recognition, 8,
     127-139.