Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-24T05:33:44.376Z Has data issue: false hasContentIssue false
  • English
  • Français

Real-time evaluation of milk quality as reflected by clotting parameters of individual cow's milk during the milking session, between day-to-day and during lactation

Published online by Cambridge University Press:  28 March 2013

Gabriel Leitner ,
Uzi Merin ,
Shamay Jacoby ,
Dror Bezman ,
Liubov Lemberskiy-Kuzin  and
Gil Katz
Gabriel Leitner*
Affiliation:
National Mastitis Reference Center, Kimron Veterinary Institute, PO Box 12, Bet Dagan 50250, Israel
Uzi Merin
Affiliation:
Department of Food Quality and Safety, Agricultural Research Organization, the Volcani Center, PO Box 6, Bet Dagan 50250, Israel
Shamay Jacoby
Affiliation:
Institute of Animal Science, Agricultural Research Organization, the Volcani Center, PO Box 6, Bet Dagan 50250, Israel
Dror Bezman
Affiliation:
Afimilk, Afikim 15148, Israel
Liubov Lemberskiy-Kuzin
Affiliation:
Afimilk, Afikim 15148, Israel
Gil Katz
Affiliation:
Afimilk, Afikim 15148, Israel
*
†E-mail: [email protected]
Get access

Abstract

Real-time analysis of milk coagulation properties as performed by the AfiLab™ milk spectrometer introduces new opportunities for the dairy industry. The study evaluated the performance of the AfiLab™ in a milking parlor of a commercial farm to provide real-time analysis of milk-clotting parameters –Afi-CF for cheese manufacture and determine its repeatability in time for individual cows. The AfiLab™ in a parlor, equipped with two parallel milk lines, enables to divert the milk on-line into two bulk milk tanks (A and B). Three commercial dairy herds of 220 to 320 Israeli Holstein cows producing ∼11 500 l during 305 days were selected for the study. The Afi-CF repeatability during time was found significant (P < 0.001) for cows. The statistic model succeeded in explaining 83.5% of the variance between Afi-CF and cows, and no significant variance was found between the mean weekly repeated recordings. Days in milk and log somatic cell count (SCC) had no significant effect. Fat, protein and lactose significantly affected Afi-CF and the empirical van Slyke equation. Real-time simulations were performed for different cutoff levels of coagulation properties where the milk of high Afi-CF cutoff value was channeled to tank A and the lower into tank B. The simulations showed that milk coagulation properties of an individual cow are not uniform, as most cows contributed milk to both tanks. Proportions of the individual cow's milk in each tank depended on the selected Afi-CF cutoff. The assessment of the major causative factors of a cow producing low-quality milk for cheese production was evaluated for the group that produced the low 10% quality milk. The largest number of cows in those groups at the three farms was found to be cows with post-intramammary infection with Escherichia coli and subclinical infections with streptococci or coagulase-negative staphylococci (∼30%), although the SCC of these cows was not significantly different. Early time in lactation together with high milk yield >50 l/day, and late in lactation together with low milk yield<15 l/day and estrous (0 to 5 days) were also important influencing factors for low-quality milk. However, ∼50% of the tested variables did not explain any of the factors responsible for the cow producing milk in the low – 10% Afi-CF.

Keywords

milk clotting parametersreal-time evaluationcheese yield
Type
Product quality, human health and well-being
Information
animal , Volume 7 , Issue 9 , September 2013 , pp. 1551 - 1558
Copyright
Copyright © The Animal Consortium 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Banks, JM, Muir, DD, Tamine, AY 1984. Equations for estimation of the efficiency of Cheddar cheese production. Dairy Industries International 49, 14–17.Google Scholar
Barbano, DM, Rasmussen, RR, Lynch, JM 1991. Influence of milk somatic cell count and milk age on cheese yield. Journal of Dairy Science 74, 369–388.CrossRefGoogle Scholar
Brito, C, Niklitschekl, H, Molina, L, Molina, I 2002. Evaluation of mathematical equations to predict the theoretical yield of Chilean Gouda cheese. International Journal of Dairy Technology 55, 32–39.CrossRefGoogle Scholar
Čandek-Potokar, M, Prevolnik, M, Babnik, D, Perpar, T 2006. The uncertainty of results when estimating daily milk records. Animal Research 55, 521–532.Google Scholar
Coulon, J-B, Hurtaud, C, Remond, B, Verité, R 1998. Factors contributing to variation in the proportion of casein in cows’ milk true protein: a review of recent INRA experiments. Journal of Dairy Research 65, 375–387.Google Scholar
Emmons, DB, Modler, HW 2010. A commentary on predictive cheese yield formulas. Journal of Dairy Science 93, 5517–5537.Google Scholar
Emmons, DB, Dube, C, Modler, HW 2003. Transfer of protein from milk to cheese. Journal of Dairy Science 86, 469–485.CrossRefGoogle ScholarPubMed
Emmons, DB, Ernstrom, CA, Lacroix, C, Sauve, P 1993. Further considerations in formulas for predicting yield from the composition of milk. Journal of Dairy Science 76, 914–920.Google Scholar
Fenelon, MA, Guinee, TP 1999. The effect of milk fat on Cheddar cheese yield and its prediction, using modifications of the Van Slyke cheese yield formula. Journal of Dairy Science 82, 2287–2299.Google Scholar
Fleminger, G, Ragones, H, Merin, U, Silanikove, N, Leitner, G 2011. Characterization of casein-derived peptides generated by bacterial enzymes during sub-clinical intramammary infection. International Dairy Journal 21, 914–920.Google Scholar
Forsbäck, L, Lindmark-Månsson, H, Andrén, A, Åkerstedt, M, Svennersten-Sjaunja, K 2009. Udder quarter milk composition at different levels of somatic cell count in cow composite milk. Animal 3, 710–717.Google Scholar
Forsbäck, L, Lindmark-Månsson, H, Andrén, A, Åkerstedt, M, Andrée, L, Svennersten-Sjaunja, K 2010. Day-to-day variation in milk yield and milk composition at the udder quarter level. Journal of Dairy Science 93, 3569–3577.CrossRefGoogle ScholarPubMed
Ikonen, T, Ahlfors, K, Kempe, R, Ojala, M, Routtinen, O 1999. Genetic parameters for the milk coagulation properties and prevalence of noncoagulating milk in Finnish dairy cows. Journal of Dairy Science 82, 205–214.Google Scholar
Ikonen, T, Morri, S, Tyrisevä, AM, Ruottinen, O, Ojala, M 2004. Genetic and phenotypic correlations between milk coagulation properties, milk production traits, somatic cell count, casein content, and pH of milk. Journal of Dairy Science 87, 458–467.Google Scholar
Lawrence, RC 1991. Cheese yield potential of milk. In Factors affecting the yield of cheese (ed. DB Emmons), IDF Special Issue No. 9301, pp. 109–120. International Dairy Federation Publication, Brussels, Belgium.Google Scholar
Leitner, G, Krifucks, O, Merin, U, Lavi, Y, Silanikove, N 2006. Interactions between bacteria type, proteolysis of casein and physico-chemical properties of bovine milk. International Dairy Journal 16, 648–654.Google Scholar
Leitner, G, Lavi, Y, Merin, U, Lemberskiy-Kuzin, L, Katz, G 2011. Online evaluation of milk quality according to coagulation properties for its optimal distribution for industrial applications. Journal of Dairy Science 94, 2923–2932.Google Scholar
Leitner, G, Merin, U, Lemberskiy-Kuzin, L, Bezman, D, Katz, G 2012. Real time visual/near-infrared analysis of milk clotting parameters for industrial applications. Animal 6, 1170–1177.CrossRefGoogle ScholarPubMed
Malacarne, M, Summer, A, Fossa, E, Formaggioni, P, Franceschi, P, Pecorari, M, Mariani, P 2006. Composition, coagulation properties and Parmigiano-Reggiano cheese yield of Italian Brown and Italian Friesian herd milks. Journal of Dairy Research 73, 171–177.Google Scholar
Oliver, SP, Gonzalez, RN, Hogan, JS, Jayarao, BM, Owens, WE 2004. Microbiological procedures for the diagnosis of bovine udder infection and determination of milk quality, 4th edition. The National Mastitis Council, Inc., Verona, WI, USA.Google Scholar
Penasa, M, Cassandro, M, Pretto, D, De Marchi, M, Comin, A, Chessa, S, Dal Zotto, R, Bittante, G 2010. Influence of composite casein genotypes on additive genetic variation of milk production traits and coagulation properties in Holstein–Friesian cows. Journal of Dairy Science 93, 3346–3349.CrossRefGoogle ScholarPubMed
Quist, MA, LeBlanc, SJ, Hand, KJ, Lazenby, D, Miglior, F, Kelton, DF 2008. Milking-to-milking variability for milk yield, fat and protein percentage, and somatic cell count. Journal of Dairy Science 91, 3412–3423.Google Scholar
Sapru, A, Barbano, DM, Yun, JJ, Klei, L, Oltenacu, PA, Bandler, DK 1997. Cheddar cheese: influence of milking frequency and stage of lactation on composition and yield. Journal of Dairy Science 80, 437–446.Google Scholar
SAS Institute 2000. JMP User's Guide. SAS Institute, Inc., Cary, NC, USA.Google Scholar
Vallas, M, Bovenhuis, H, Kaart, T, Pärna, K, Kiiman, H, Pärna, E 2010. Genetic parameters for milk coagulation properties in Estonian Holstein cows. Journal of Dairy Science 93, 3789–3796.Google Scholar
Verdier-Metz, I, Coulon, JB, Pradel, P 2001. Relationship between milk fat and protein contents and cheese yield. Animal Research 50, 365–371.Google Scholar
Wedholm, A, Larsen, LB, Lindmark-Månsson, H, Karlsson, AH, Andrén, A 2006. Effect of protein composition on the cheese-making properties of milk from individual dairy cows. Journal of Dairy Science 89, 3296–3305.Google Scholar