Accuracy of in-line milk composition analysis with diffuse reflectance near-infrared spectroscopy

Journal of Dairy Science

Volumn 95, Issue 11, 2012, Pages 6465-6476

  Melfsen, A ^a   Hartung, E ^a   Haeussermann, A ^a  

^a UNIVERSITY OF KIEL   (Germany)

Author keywords

Diffuse reflectance near infrared spectroscopy; In line milk analysis; Reproducibility; Somatic cell count classification

Indexed keywords

FAT; LACTOSE; MILK PROTEIN; UREA;

ANIMAL; ANIMAL DISEASE; ARTICLE; CATTLE; CELL COUNT; CHEMISTRY; CYTOLOGY; DAIRYING; FEMALE; INSTRUMENTATION; METHODOLOGY; MILK; NEAR INFRARED SPECTROSCOPY;

ANIMALS; CATTLE; CELL COUNT; DAIRYING; FATS; FEMALE; LACTOSE; MILK; MILK PROTEINS; SPECTROSCOPY, NEAR-INFRARED; UREA;

BOS;

EID: 84867746777     PISSN: 00220302     EISSN: 15253198     Source Type: Journal    
DOI: 10.3168/jds.2012-5388     Document Type: Article

References (44)

1. ADR (Arbeitsgemeinschaft Deutscher Rinderzüchter). 2002. ADR-Empfehlung 1.13: Referenzmethode zur Bestimmung des Harnstoffgehaltes in Milch, Kontinuierliche Durchflussanalyse [ADR Recommendation 1:13 reference method for determination of urea content in milk, continuous flow analysis]. Arbeitsgemeinschaft Deutscher Rinderzüchter, Bonn, Germany.
2. Aernouts B., Polshin E., Lammertyn J., Saeys W. Visible and near-infrared spectroscopic analysis of raw milk for cow health monitoring: Reflectance or transmittance?. J. Dairy Sci. 2011, 94:5315-5329.
3. Blanco M., Valdes D. Influence of temperature on the predictive ability of near infrared spectroscopy models. J. Near Infrared Spectrosc. 2004, 12:121-126.
4. Bruckmaier R.M., Ontsouka C.E., Blum J.W. Fractionized milk composition in dairy cows with subclinical mastitis. Vet. Med. (Praha) 2004, 49:283-290.
5. Chen J.Y., Iyo C., Terada F., Kawano S. Effect of multiplicative scatter correction on wavelength selection for near infrared calibration to determine fat content in raw milk. J. Near Infrared Spectrosc. 2002, 10:301-307.
6. Dan P., Kaina D., Xia L., Xu K. Multidimensional calibration of near infrared spectra for component concentration measurement in milk at different temperatures. 2010 4th Int. Conf. Bioinform. Biomed. Eng. (iCBBE). 2010, IEEE Press, Piscataway, NJ, Pages 1-4.
7. Detilleux J., Arendt J., Lomba F., Leroy P. Methods for estimating areas under receiver-operating characteristic curves: Illustration with somatic-cell scores in subclinical intramammary infections. Prev. Vet. Med. 1999, 41:75-88.
8. Leitlinien zur Bekämpfung der Mastitis des Rindes als Herdenproblem 1994, DVG (Deutsche Veterinärmedizinische Gesellschaft), Deutsche Veterinärmedizinische Gesellschaft, Giessen, Germany, vol 3. Auflage [Guidelines for the control of bovine mastitis as a herd problem]. 3rd.
9. Leitlinien zur Bekämpfung der Mastitis des Rindes als Bestandsproblem. Sachverständigenausschuss "Subklinische Mastitis" [Guidelines for the control of bovine mastitis as a stock problem 2002, DVG (Deutsche Veterinärmedizinische Gesellschaft), In the Committee of Experts on "Subclinical Mastitis."]. Deutsche Veterinärmedizinische Gesellschaft, Hannover, Germany, Pages 3-29.
10. Friggens N.C., Ridder C., Lovendahl P. On the use of milk composition measures to predict the energy balance of dairy cows. J. Dairy Sci. 2007, 90:5453-5467.
11. Harmon R.J. Physiology of mastitis and factors affecting somatic cell counts. J. Dairy Sci. 1994, 77:2103-2112.
12. Heuer C., Luinge H.J., Lutz E.T.G., Schukken Y.H., van der Maas J.H., Wilmink H., Noordhuizen J.P.T.M. Determination of acetone in cow milk by Fourier transform infrared spectroscopy for the detection of subclinical ketosis. J. Dairy Sci. 2001, 84:575-582.
13. Heuer C., Schukken Y.H., Dobbelaar P. Postpartum body condition score and results from the first test day milk as predictors of disease, fertility, yield, and culling in commercial dairy herds. J. Dairy Sci. 1999, 82:295-304.
14. Heuer C., Van Straalen W.M., Schukken Y.H., Dirkzwager A., Noordhuizen J.P.T.M. Prediction of energy balance in high yielding dairy cows with test-day information. J. Dairy Sci. 2001, 84:471-481.
15. Hillerton, J. E. 1999. Redefining mastitis based on somatic cell count. Pages 4-6 in Int. Dairy Fed. (IDF) Bull. No. 345. Int. Dairy Fed., Brussels, Belgium.
16. Huppertz T., Kelly A.L., Fox P.F., McSweeney P.L.H. Physical chemistry of milk fat globules. Advanced Dairy Chemistry. Vol. 2: Lipids 2006, Springer, New York, NY, Pages 173-212.
17. Section 13-Guidelines for on-line milk analysis. International Agreement of Recording Practices-Guidelines Approved by the General Assembly Held in Riga, Latvia, on June 2010 2010, ICAR (International Committee for Animal Recording), ICAR Sub-Comm. Milk Analysis, Int. Comm. Anim. Rec., Rome, Italy, Pages 325-351.
18. IDF (International Dairy Federation). 1967. Definition of mastitis. In Ann. Bull. Part III. Int. Dairy Fed., Brussels, Belgium.
19. IDF (International Dairy Federation). 1987. Bovine mastitis-Definition and guidelines for diagnosis. In IDF Doc. Bull. 211. Int. Dairy Fed., Brussels, Belgium.
20. (International Dairy Federation). 2000. Standard 141C: Whole milk-Determination of milkfat, protein and lactose content-Guidance on the operation of mid-infrared instruments. Int. Dairy Fed., Brussels, Belgium.
21. ISO (International Organization for Standardization). 1994. ISO 5725-1: Accuracy (trueness and precision) of measurement methods and results-Part 1: General principles and definitions. 5725-1. Int. Organ. Stand., Geneva, Switzerland.
22. ISO (International Organization for Standardization). 1999. ISO 9622: Whole milk-Determination of milkfat, protein and lactose content-Guidance on the operation of mid-infrared instruments. Int. Organ. Stand., Geneva, Switzerland.
23. Jääskeläinen A.J., Peiponen K.E., Räty J.A. On reflectometric measurement of a refractive index of milk. J. Dairy Sci. 2001, 84:38-43.
24. Kamphuis C., Sherlock R., Jago J., Mein G., Hogeveen H. Automatic detection of clinical mastitis is improved by in-line monitoring of somatic cell count. J. Dairy Sci. 2008, 91:4560-4570.
25. Kawasaki M., Kawamura S., Tsukahara M., Morita S., Komiya M., Natsuga M. Near-infrared spectroscopic sensing system for on-line milk quality assessment in a milking robot. Comput. Electron. Agric. 2008, 63:22-27.
26. Maltz E., Antler A., Halachmi I., Schmilovitch Z. Precision concentrate rationing to the dairy cow using on-line daily milk composition sensor, milk yield and body weight. Precision Livestock Farming '09 2009, Wageningen Academic Publishers, Wageningen, the Netherlands, Pages 17-23.
27. McParland S., Banos G., Wall E., Coffey M.P., Soyeurt H., Veerkamp R.F., Berry D.P. The use of mid-infrared spectrometry to predict body energy status of Holstein cows. J. Dairy Sci. 2011, 94:3651-3661.
28. Melfsen A., Hartung E., Haeussermann A. Accuracy of milk composition analysis with near infrared spectroscopy in diffuse reflectance mode. Biosystems Eng. 2012, 112:210-217.
29. Guidelines on Normal and Abnormal Raw Milk Based on SCC and Signs of Clinical Mastitis. Natl 2001, NMC (National Mastitis Council), Mastitis Counc., Madison, WI.
30. Purnomoadi A., Batajoo K.K., Ueda K., Terada F. Influence of feed source on determination of fat and protein in milk by near-infrared spectroscopy. Int. Dairy J. 1999, 9:447-452.
31. Saranwong S., Kawano S. System design for non-destructive near infrared analyses of chemical components and total aerobic bacteria count of raw milk. J. Near Infrared Spectrosc. 2008, 16:389-398.
32. Schalm O.W., Noorlander D.O. Experiments and observations leading to development of the California mastitis test. J. Am. Vet. Med. Assoc. 1957, 130:199-204.
33. Schmilovitch Z., Shmulevich I., Notea A., Maltz E. Near infrared spectrometry of milk in its heterogeneous state. Comput. Electron. Agric. 2000, 29:195-207.
34. Schukken Y.H., Wilson D.J., Welcome F., Garrison-Tikofsky L., Gonzalez R.N. Monitoring udder health and milk quality using somatic cell counts. Vet. Res. 2003, 34:579-596.
35. SvennerstenSjaunja K., Sjaunja L.O., Bertilsson J., Wiktorsson H. Use of regular milking records versus daily records for nutrition and other kinds of management. Livest. Prod. Sci. 1997, 48:167-174.
36. Toni F., Vincenti L., Grigoletto L., Ricci A., Schukken Y.H. Early lactation ratio of fat and protein percentage in milk is associated with health, milk production, and survival. J. Dairy Sci. 2011, 94:1772-1783.
37. Töpel, A. 2004. Chemie und Physik der Milch: Naturstoff- Rohstoff- Lebensmittel. Behr, Hamburg, Germany.
38. Tsenkova R., Atanassova S., Kawano S., Toyoda K. Somatic cell count determination in cow's milk by near-infrared spectroscopy: A new diagnostic tool. J. Anim. Sci. 2001, 79:2550-2557.
39. Tsenkova R., Atanassova S., Morita H., Ikuta K., Toyoda K., Iordanova I.K., Hakogi E. Near infrared spectra of cows' milk for milk quality evaluation: Disease diagnosis and pathogen identification. J. Near Infrared Spectrosc. 2006, 14:363-370.
40. Tsenkova R., Atanassova S., Ozaki Y., Toyoda K., Itoh K. Near-infrared spectroscopy for biomonitoring: Influence of somatic cell count on cow's milk composition analysis. Int. Dairy J. 2001, 11:779-783.
41. Tsenkova R., Atanassova S., Toyoda K., Ozaki Y., Itoh K., Fearn T. Near-infrared spectroscopy for dairy management: Measurement of unhomogenized milk composition. J. Dairy Sci. 1999, 82:2344-2351.
42. Tsenkova R., Meilina H., Kuroki S., Burns D.H. Near infrared spectroscopy using short wavelengths and leave-one-cow-out cross-validation for quantification of somatic cells in milk. J. Near Infrared Spectrosc. 2009, 17:345-351.
43. Williams P.C. Implementation of near-infrared technology. Near-Infrared Technology in the Agricultural and Food Industries 2001, 145-170. Am. Assoc. Cereal Chem., St. Paul, MN. P. Williams, K.H. Norris (Eds.).
44. Williams P.C., Norris K. Variables affecting near-infrared spectroscopic analysis. Near-Infrared Technology in the Agricultural and Food Industries 2001, 171-185. Am. Assoc. Cereal Chem., St. Paul, MN. P. Williams, K.H. Norris (Eds.).