Journal of Dairy Science
Volume 93, Issue 2 , Pages 427-436 , February 2010

Invited review: Technical solutions for analysis of milk constituents and abnormal milk

Received 14 July 2009 ,Accepted 19 October 2009.

References 

  1. Auldist M. Effect on processing characteristics. In:  Roginski H,  Fuquay JW,  Fox PF editor. Encyclopedia of Dairy Science. London, UK: Academic Press Inc.; 2002;p. 2002–2006
  2. Barth K, Fischer R, Worstorff H. Evaluation of variation in conductivity during milking to detect subclinical mastitis in cows milked by robotic systems. In:  Hogeveen H,  Meijering A editor. Proc. Int. Symp. on Robotic Milking. Wageningen, the Netherlands: Wageningen Pers; 2000;p. 89–96
  3. Bruckmaier RM, Ontsouka CE, Blum JW. Fractionized milk composition in dairy cows with subclinical mastitis. Vet. Med. – Czech. 2004;49:283–290
  4. Bürger C, Nacke T. Zellzahlbestimmung in der Rohmilch. Landtechnik. 2005;60:160–161
  5. Casalinuovo IA, Di Pierro D, Coletta M, Di Francesco P. Application of electronic noses for disease diagnosis and food spoilage detection. Sensors. 2006;6:1428–1439
  6. Cavero D, Tölle K-H, Buxadé C, Krieter J. Mastitis detection in dairy cows by application of fuzzy logic. Livest. Sci. 2006;105:207–213
  7. Cavero D, Tölle K-H, Rave G, Buxadé C, Krieter J. Analysing serial data for mastitis detection by means of local regression. Livest. Sci. 2007;110:101–110
  8. Čejna V, Chládek G. The importance of monitoring changes in milk fat to protein ratio in Holstein cows during lactation. J. Central Eur. Agric. 2005;6:539–545
  9. Chagunda MGG, Friggens NC, Rasmussen MD, Larsen T. A model for detection of individual cow mastitis based on an indicator measured in milk. J. Dairy Sci. 2006;89:2980–2998
  10. Claycomb RW, Delwiche MJ. Biosensor for on-line measurement of bovine progesterone during milking. Biosens. Bioelectron. 1998;13:1173–1180
  11. Dodd FH, Booth JM. Mastitis and milk production. In:  Andrews AH editors. The Health of Dairy Cattle. Hoboken, NJ: Blackwell Science; 2000;p. 213–255
  12. CB European Commission. 2004. EC 853/2004 of the European parliament and of the council of 29 April 2004 laying down specific hygiene rules for food of animal origin. European Union, Brussels, Belgium.
  13. Elliott-Martin RJ, Mottram TT, Gardner JW, Hobbs PJ, Bartlett PN. Preliminary investigation of breath sampling as a monitor of health in dairy cattle. J. Agric. Eng. Res. 1997;67:267–275
  14. Eriksson Ǻ, Persson Waller K, Svennersten-Sjaunja K, Haugen J-E, Lundby F, Lind O. Detection of mastitic milk using a gas-sensor array system (electronic nose). Int. Dairy J. 2005;15:1193–2120
  15. Eshkenazi I, Maltz E, Zion B, Rishpon J. A three-cascaded-enzymes biosensor to determine lactose concentration in raw milk. J. Dairy Sci. 2000;83:1939–1945
  16. Espada E, Vijverberg H. Milk colour analysis as a tool for the detection of abnormal milk. In: Proceedings of the First North American Conference on Robotic Milking, Wageningen, the Netherlands. Wageningen, the Netherlands: Wageningen Academic Press; 2002;p. IV29–IV38
  17. Fahr R-D. Tier- und umweltbedingte Einflussfaktoren auf die milchleisstung, milchinhaltsstoffe und qualitätsmerkmale. In:  Fahr RD,  Lengerken GV editor. Milcherzeugung. Frankfurt am Main, Germany: Deutscher Fachverlag GmbH; 2003;p. 102–124
  18. Friggens NC, Chagunda MGG. Prediction of the reproductive status of cattle on the basis of milk progesterone measures: Model description. Theriogenology. 2005;64:155–190
  19. Friggens NC, Chagunda MGG, Bjerring M, Ridder C, Hoisgaard S, Larsen T. Estimating degree of mastitis from time-series measurements in milk: A test of a model based on lactate dehydrogenase measurements. J. Dairy Sci. 2007;90:5415–5427
  20. Gonzalo C, Lingage B, Carriedo JA, de la Fuente F, San Primitivo F. Evaluation of the overall accuracy of the DeLaval cell counter for somatic cell counts in ovine milk. J. Dairy Sci. 2006;89:4613–4619
  21. Gustafsson AH. Acetone and Urea Concentration in Milk as Indicators of the Nutritional Status and the Composition of the Diet of Dairy Cows. PhD Diss. Uppsala: Swedish University of Agricultural Sciences; 1993;
  22. Halm H. Zum Einfluss eines Automatischen Melkverfahrens auf Milchmengenleistung und Milchinhaltsstoffe Hochleistender DH-Kühe unter Berücksichtigung von Laktationsstadium und Eutergesundheit. PhD Thesis. Hannover, Germany: School for Veterinary Medicine; 2003;
  23. Hamann J. Diagnosis of mastitis and indicators of milk quality. In:  Hogeveen H editors. Mastitis in Dairy Production: Current Knowledge and Future Solutions. Wageningen, the Netherlands: Wageningen Academic Publishers; 2005;p. 82–91
  24. Hamann J, Fehlings K. Leitlinien zur Bekämpfung der Mastitis des Rindes als Bestandsproblem. 4th ed.. Gießen, Germany: Verlag der Deutschen Veterinärmedizinische Gesellschaft E.V. (DVG); 2002;
  25. Hassan KJ, Samarasinghe S, Lopez-Benavides MG. Use of neural networks to detect minor and major pathogens that cause bovine mastitis. J. Dairy Sci. 2009;92:1493–1499
  26. Hettinga KA, van Valenberg HJF, Lam TJGM, van Hooijdonk ACM. Detection of mastitis pathogens by analysis of volatile bacterial metabolites. J. Dairy Sci. 2008;91:3834–3839
  27. Hogeveen H, Ouweltjes W. Automatic on-line detection of abnormal milk. In:  Roginski H,  Fuquay JW,  Fox PF editor. Encyclopedia of Dairy Science. London, UK: Academic Press Inc.; 2002;p. 1735–1740
  28. Jenkins DM, Delwiche MJ. Manometric biosensor for on-line measurement of milk urea. Biosens. Bioelectron. 2002;17:557–563
  29. Kamphuis C, Pietersma D, van der Tol R, Wiedemann M, Hogeveen H. Using sensor data patterns from an automatic milking system to develop predictive variables for classifying clinical mastitis and abnormal milk. Comput. Electron. Agric. 2008;62:169–181
  30. 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
  31. Katz G, Arazi A, Pinsky N, Halachmi I, Schmilovitch Z, Aizinbud E, et al. Current and near term technologies for automated recording of animal data for precision dairy farming. J. Anim. Sci. 2007;85(Suppl. 1):377
  32. 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;62:22–27
  33. Kelly AL. Test methods and standards. In:  Roginski H,  Fuquay JW,  Fox PF editor. Encyclopedia of Dairy Science. London, UK: Academic Press Inc.; 2002;p. 1995–2001
  34. Kirchgessner M. Tierernährung. 10th ed.. Frankfurt, Germany: DLG-Verlags-GmbH; 1997;
  35. Kirchgessner M, Kreuzer M, Roth-Maier DA. Milk urea and protein content to diagnose energy and protein malnutrition of dairy cows. Arch. Anim. Nutr. 1986;36:192–197
  36. Kitchen BJ, Middleton G, Kwee WS, Andrews RJ. N-acetyl-beta-D-glucosaminidase (NAGase) levels in bulk herd milk. J. Dairy Res. 1984;48:167–188
  37. Krehl I, Brunsch R. Evaluation of ions sodium and potassium in milk as a criteria of change of blood-milk-barrier—A lactation study. In:  Cox S editors. Precision Livestock Farming’05. Wageningen, the Netherlands: Wageningen Academic Publishers; 2005;p. 149–155
  38. Lane AJP, Wathes DC. An electronic nose to detect changes in perineal odors associated with estrus in the cow. J. Dairy Sci. 1998;81:2145–2150
  39. Linker R, Etzion Y. Potential and limitation of mid-infrared attenuated total reflectance spectroscopy for real time analysis of raw milk in milking lines. J. Dairy Res. 2009;76:42–48
  40. Maasen-Francke B, Wiethoff M, Suhr O, Clemens C, Knoll A. A method to detect flakes and clots in milk in automatic milking systems. In:  Meijering A,  Hogeveen H,  de Koning CJAM editor. Automatic Milking—A Better Understanding. Wageningen, the Netherlands: Wageningen Academic Publishers; 2004;p. 251
  41. Mills R. On farm analysis. An eye on the future. Foss in Focus. 2008;32:18–20
  42. Moon JS, Koo HC, Joo YS, Jeon SH, Hur DS, Chung CI, et al. Application of a new portable microscopic somatic cell counter with disposable plastic chip for milk analysis. J. Dairy Sci. 2007;90:2253–2259
  43. Mottram T, Rudnitskaya A, Legin A, Fitzpatrick JL, Eckersall PD. Evaluation of a novel chemical sensor system to detect clinical mastitis in bovine milk. Biosens. Bioelectron. 2007;22:2689–2693
  44. Mottram T, Velasco-Garcia M, Berry P, Richards P, Ghesquiere J, Masson L. Automatic on-line analysis of milk constituents (urea, ketones, enzymes and hormones) using biosensors. Comp. Clin. Pathol. 2002;11:50–58
  45. Mottram, T., and M. N. Velasco-Garcia. 2004. Development and testing of on-line biosensors for automated fertility management of dairy cows. Page 14 in 3rd International Workshop on Smart Sensors in Livestock Monitoring, September 10–11, 2004. (CD) Laboratory for Agricultural Buildings Research, K. U. Leuven, Leuven, Belgium.
  46. Nielsen NI, Friggens NC, Chagunda MGG, Ingvartsen KL. Predicting risk of ketosis in dairy cows using in-line measurements of β-hydroxybutyrate: A biological model. J. Dairy Sci. 2005;88:2441–2453
  47. Nielsen NI, Larsen T, Bjerring M, Ingvartsen KL. Quarter health, milking interval, and sampling time during milking affect the concentration of milk constituents. J. Dairy Sci. 2005;88:3186–3200
  48. Ordolff D. Introduction of electronics into milking technology. Comput. Electron. Agric. 2001;30:125–149
  49. Ordolff, D. 2001b. Einsatz von Farbmessung zur Bewertung von Vorgemelken. Bau, Technik und Umwelt in der Landwirtschaftlichen Nutztierhaltung, Hohenheim, Germany, March 6–7, 2001. KTBL, Darmstadt, Germany.
  50. Ordolff D. Veränderung der Milchbeschaffenheit zu Laktationsbeginn. Bau, Technik und Umwelt in der Landwirtschaftlichen Nutztierhaltung, Vechta, Germany. Germany: KTBL, Darmstadt; 2003;
  51. Ordolff D. Untersuchungen zur bewertung der milchqualität durch mobile systeme zur milchanalyse. Bericht des Instituts für Betriebstechnik und Bauforschung. Jahresbericht 2005. Bundesforschungsanstalt für Landwirtschaft (FAL). Germany: Braunschweig; 2005;Page 134
  52. Ordolff D. Aussagekraft von standard-milchinhaltsstoffen zur bewertung der eutergesundheit. Landtechnik. 2006;61:48–49
  53. Pache S. Elektronikeinsatz zur gesundheits und fruchtbarkeitsüberwachung. Precision Dairy Farming. KTBL-Schrift 457. Germany: KTBL, Darmstadt; 2007;Pages 101–113
  54. Park YK, Koo HC, Kim SH, Hwang SY, Jung WK, Kim JM, et al. The analysis of milk components and pathogenic bacteria isolated from bovine raw milk in Korea. J. Dairy Sci. 2007;90:5405–5414
  55. Pemberton RM, Hart JP, Mottram TT. An assay for the enzyme N-acetyl-β-D-glucosaminidase (NAGase) based on electrochemical detection using screen-printed carbon electrodes (SPCEs). Analyst (Lond.). 2001;126:1866–1871
  56. Pemberton RM, Hart JP, Mottram TT. An electrochemical immunosensor for milk progesterone using a continuous flow system. Biosens. Bioelectron. 2001;16:715–723
  57. Rasmussen MD. Detection and separation of abnormal milk in automatic milking systems. In:  Meijering A,  Hogeveen H,  de Koning CJAM editor. Automatic Milking—A Better Understanding. Wageningen, the Netherlands: Wageningen Academic Publishers; 2004;p. 189–197
  58. Rasmussen MD, Wiking L, Bjerring M, Larsen HC. Influence of air intake on the concentration of free fatty acids and vacuum fluctuations during automatic milking. J. Dairy Sci. 2006;89:4596–4605
  59. Rodrigues ACO, Cassoli LD, Machado PF, Ruegg PL. Short communication: Evaluation of an on-farm test to estimate somatic cell count. J. Dairy Sci. 2009;92:990–995
  60. Rudnitskaya A, Legin A. Sensor systems, electronic tongues and electronic noses, for the monitoring of biotechnical process. J. Ind. Microbiol. Biotechnol. 2008;35:443–451
  61. Sarikaya H, Bruckmaier RM. Importance of the sampled milk fraction for the prediction of total quarter somatic cell count. J. Dairy Sci. 2006;89:4246–4250
  62. Schalm OW, Noorlander DO. Experiments and observations leading to the development of the California mastitis test. J. Am. Vet. Med. Assoc. 1957;130:199–204
  63. Schmilovitch, Z., G. Katz, E. Maltz, M. Kutscher, M. Sarig, I. Halachmi, A. Hoffman, H. Egozi, and E. Unar. 2007. Spectroscopic fluid analyzer. US Patent no. 7236237.
  64. Schmilovitch Z, Shmulevich I, Notea A, Maltz E. Near infrared spectrometry of milk in its heterogeneous state. Comput. Electron. Agric. 2000;29:195–207
  65. Sienkiewicz T, Kirst E. Analytik von Milch und Milcherzeugnissen. Hamburg, Germany: Behr's Verlag; 2006;
  66. Simersky R, Swaczynova J, Morris DA, Franek M, Strnad M. Development of an ELISA-based kit for the on-farm determination of progesterone in milk. Vet. Med. (Praha). 2007;52:19–28
  67. Sjaunja L-O. A review of spectroscopic methods and their suitability as analytical techniques for farm testing. In:  Cox S editors. Precision Livestock Farming '05. Wageningen, the Netherlands: Wageningen Academic Publishers; 2005;p. 25–32
  68. Soyeurt H, Dardenne P, Dehareng F, Lognay G, Veselko D, Marlier M, et al. Estimating fatty acid content in cow milk using mid-infrared spectrometry. J. Dairy Sci. 2006;89:3690–3695
  69. Svennersten-Sjaunja K, Sjögren M, Andersson I, Sjaunja L-O. Milk analyses: A comparison between simple IR-instrument for use on farm level and available IR-methods. In:  Cox S editors. Precision Livestock Farming '05. Wageningen, the Netherlands: Wageningen Academic Publishers; 2005;p. 141–147
  70. Thompson DI, Postle DS. The Wisconsin mastitis test—An indirect estimation of leukocytes in milk. J. Milk Food Technol. 1964;27:271–275
  71. Thomson NA, Woolford MW, Copeman PJA, Auldist MJ. Milk harvesting and cow factors influencing seasonal variation in the levels of free fatty acids in milk from Waikato dairy herds. N.Z. J. Agric. Res. 2005;48:11–21
  72. Töpel A. Chemie und Physik der Milch. 1st ed.. Hamburg, Germany: B. Behr's Verlag GmbH & Co. KG; 2004;
  73. Trilk J, Münch K, Franke C. Untersuchungen zur feststellung von eutergesundheitsstörungen und Rohmilchveränderungen mit dem MQC und weiteren technischen einrichtungen beim automatischen melksystem Lely Astronaut. Schriftenreihe des Landesamtes für Verbraucherschutz, Landwirtschaft und Flurneuordnung, Reihe Landwirtschaft, Band 7 Heft V. MLUV. Germany: Brandenburg; 2006;80–89
  74. Tsenkova R, Atanassova S, Itoh K, Ozaki Y, Toyoda K. Near infrared spectroscopy for biomonitoring: Cow milk composition measurement in a spectral region from 1,100 to 2,400 nanometers. J. Anim. Sci. 2000;78:515–522
  75. 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
  76. Tsenkova R, Atanassova S, Morita H, Ikuta K, Toyoda K, Iordanova IK, et al. Near infrared spectra of cow's milk for milk quality evaluation: Disease diagnosis and pathogen identification. J. Near Infrared Spectroscopy. 2006;14:363–370
  77. 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
  78. van Duinkerken G, André G, Smith MCJ, Monteny GJ, Sebek LBJ. Effect of rumen-degradable protein balance and forage type on bulk milk urea concentration and emission of ammonia from dairy cow houses. J. Dairy Sci. 2005;88:1099–1112
  79. Whyte DS, Orchard RG, Cross P, Frietsch T, Claycomb RW, Mein GA. An on-line somatic cell count sensor. In:  Meijering A,  Hogeveen H,  de Koning CJAM editor. Automatic Milking—A Better Understanding. Wageningen, the Netherlands: Wageningen Academic Publishers; 2004;p. 235–240
  80. Wiedemann M. Überwachung der Eutergesundheit bei Milchkühen durch Kombination Verschiedener Chemisch-Physikalischer Messwerte. PhD Thesis. Germany: Technische Universität München; 2004;
  81. Wiedemann M, Wendl F. The use of spectral photometry for detection of mastitis milk. In:  Meijering A,  Hogeveen H,  de Koning CJAM editor. Automatic Milking—A Better Understanding. Wageningen, the Netherlands: Wageningen Academic Publishers; 2004;p. 229–233
  82. Woo YA, Terazawa Y, Chen JY, Iyo C, Terada F, Kawano S. Development of a new measurement unit (MilkSpec-1) for rapid determination of fat, lactose, and protein in raw milk using near infrared transmittance spectroscopy. Appl. Spectrosc. 2002;56:599–604
  83. Woodcock T, Downey G, O’Donell CP. Better quality food and beverages: The role of near infrared spectroscopy. J. Near Infrared Spectroscopy. 2008;16:1–29

PII: S0022-0302(10)71486-7

doi: 10.3168/jds.2009-2565

Journal of Dairy Science
Volume 93, Issue 2 , Pages 427-436 , February 2010