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Potential and limitation of mid-infrared attenuated total reflectance spectroscopy for real time analysis of raw milk in milking lines

Published online by Cambridge University Press:  17 October 2008

Raphael Linker  and
Yael Etzion
Raphael Linker*
Affiliation:
Division of Environmental, Water and Agricultural Engineering, Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa, 32000Israel
Yael Etzion
Affiliation:
Division of Environmental, Water and Agricultural Engineering, Faculty of Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa, 32000Israel
*
*For correspondence; e-mail: linkerr@tx.technion.ac.il
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Abstract

Real-time information about milk composition would be very useful for managing the milking process. Mid-infrared spectroscopy, which relies on fundamental modes of molecular vibrations, is routinely used for off-line analysis of milk and the purpose of the present study was to investigate the potential of attenuated total reflectance mid-infrared spectroscopy for real-time analysis of milk in milking lines. The study was conducted with 189 samples from over 70 cows that were collected during an 18 months period. Principal component analysis, wavelets and neural networks were used to develop various models for predicting protein and fat concentration. Although reasonable protein models were obtained for some seasonal sub-datasets (determination errors <~0·15% protein), the models lacked robustness and it was not possible to develop a model suitable for all the data. Determination of fat concentration proved even more problematic and the determination errors remained unacceptably large regardless of the sub-dataset analyzed or of the spectral intervals used. These poor results can be explained by the limited penetration depth of the mid-infrared radiation that causes the spectra to be very sensitive to the presence of fat globules or fat biofilms in the boundary layer that forms at the interface between the milk and the crystal that serves both as radiation waveguide and sensing element. Since manipulations such as homogenisation are not permissible for in-line analysis, these results show that the potential of mid-infrared attenuated total reflectance spectroscopy for in-line milk analysis is indeed quite limited.

Keywords

Proteinfatmilk qualitychemometrics
Type
Research Article
Information
Journal of Dairy Research , Volume 76 , Issue 1 , February 2009 , pp. 42 - 48
Copyright
Copyright © Proprietors of Journal of Dairy Research 2008

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References

Chalus, P, Walter, S & Ulmschneider, M 2007 Combined wavelet transform-artificial neural network use in tablet active content determination by near-infrared spectroscopy. Analytica Chimica Acta 591 219–224CrossRefGoogle ScholarPubMed
Chevanan, N & Muthukumarappan, K 2007 Effect of calcium and phosphorus, residual lactose, and salt-to-moisture ration on the melting characteristics an hardness of cheddar during ripening. Journal of Food Science 72 E168–E176CrossRefGoogle Scholar
Coates, J 2000 Interpretation of infrared spectra, a practical approach. Encyclopedia of Analytical Chemistry, Meyers, RA Ed. John Wiley & Sons, Chichester, UK. Available at: http://www.spectroscopynow.com/FCKeditor/UserFiles/File/specNOW/eac10815.pdfGoogle Scholar
Dousseau, F, Therrien, M & Pezolet, M 1989 On the spectral subtraction of water from the FT-IR spectra of aqueous-Solutions of proteins. Applied Spectroscopy 43 538–542CrossRefGoogle Scholar
Ehrentreich, F 2002 Wavelet transform applications in analytical chemistry. Analytical and Bioanalytical chemistry 372 115–121CrossRefGoogle ScholarPubMed
Etzion, Y, Linker, R, Cogan, U & Shmulevich, I 2004 Determination of protein concentration in raw milk by mid-infrared Fourier transform infrared/attenuated total reflectance spectroscopy. Journal of Dairy Science 87 2779–2788CrossRefGoogle ScholarPubMed
Figueiredo dos Santos, RN, Galvao, RKH, Araujo, MCU & Cirino da Silva, E 2007 Improvement of prediction ability of PLS models employing the wavelet packet tranform: A case study concerning FT-IR determination of gasoline parameters. Talanta 71 1136–1143CrossRefGoogle Scholar
Hallab, R, Kohen, C, Grandison, MA, Lewis, MJ & Grandison, AS 2007 Assessment of the quality of cottage cheese produced from standard and protein-fortified skim milk. International Journal of Dairy Technology 60 69–73CrossRefGoogle Scholar
Hastie, T, Tibshirani, R & Friedman, J 2001 The elements of statistical learning. Data mining, inference, and prediction. Springer-Verlag NY, USACrossRefGoogle Scholar
Haykin, S 1999 Neural networks. A Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ, USAGoogle Scholar
Hewavitharana, AK & van Brakel, B 1997 Fourier transform infrared spectrometric method for the rapid determination of casein in raw milk. Analyst 122 701–704CrossRefGoogle Scholar
Iñón, FA, Garrigues, S & de la Guardia, M 2004 Nutritional parameters of commercially available milk samples by FTIR and chemometric techniques. Analytica Chimica Acta 513 401–412CrossRefGoogle Scholar
Jahn, BR, Linker, R, Upadhyaya, SK, Shaviv, A, Slaughter, DC & Shmulevich, I 2006 Mid-infrared spectroscopic determination of soil nitrate content. Biosystems Engineering 94 505–515CrossRefGoogle Scholar
Leung, AK, Chau, FT, Gao, JB & Shih, TM 1998 Application of wavelet transform in infrared spectrometry: spectral compression and library search. Chemometrics and Intelligent Laboratory Systems 43 69–88CrossRefGoogle Scholar
Linker, R 2007 Soil classification via mid-infrared spectroscopy. Proceedings of the First International Conference on Computer and Computing Technologies in Agriculture. Wuyishan, ChinaGoogle Scholar
Liu, Y & Brown, SD 2004 Wavelet multiscale regression from the perspective of data fusion: new conceptual approaches. Analytical and Bioanalytical Chemistry 380 445–452CrossRefGoogle ScholarPubMed
O'Farrell, IP, Sheehan, JJ, Wilkinson, MG, Harrington, D & Kelly, AL 2002 Influence of addition of plasmin or mastitic milk to cheesemilk on quality of smear-ripened cheese. Lait 82 305–316CrossRefGoogle Scholar
Reinemann, DJ & Helgren, J 2004 Online milk sensing issues for automatic milking, ASAE/CSAE Meeting paper No. 04-4191. St. Joseph, MichGoogle Scholar
Schmilovitch, Z, Notea, A & Maltz, E 2000 Near infrared spectrometry of milk in its heterogeneous state. Computers and Electronics in Agriculture 29 195–207CrossRefGoogle Scholar
Stuart, B 1997 Biological applications of infrared spectroscopy. John Wiley & Sons, Buffins Lane, Chichester, EnglandGoogle Scholar
Swaisgood, HE 1996 Characteristics of milk. Pages 841–876 in Food Chemistry. 3rd ed.Fennema, O. R. ed. Marcel Dekker, New York, USAGoogle Scholar
Trygg, J & Wold, S 1998 PLS regression on wavelet compressed NIR spectra. Chemometrics and Intelligent Laboratory Systems 42 209–220CrossRefGoogle Scholar
Walczak, B & Massart, DL 1997 Noise suppression and signal compression using the wavelet packet transform. Chemometrics and Intelligent Laboratory Systems 36 81–94CrossRefGoogle Scholar
Woo, YA, Terazawa, Y, Chen, JY, Iyo, C, Terada, F & Kawano, S 2002 Development of a new measurement unit (MilkSpec-1) for rapid determination of fat, lactose, and protein in raw milk using near-infrared transmittance spectroscopy. Applied Spectroscopy 56 599–604CrossRefGoogle Scholar