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Non-destructive Somatic Cell Count Measurement Using Near-Infrared Spectra of Milk in the 400–1,100 nm Short Wavelength Region

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Aquaphotomics for Bio-diagnostics in Dairy

Abstract

This chapter will show the results of applying short wavelength near-infrared spectroscopy (SW-NIRS) for measuring the somatic cell count (SCC) in non-homogenized, raw milk using the multiple linear regression method. Near-infrared (NIR) spectra of individual samples of non-homogenized milk from seven cows were measured daily using test tubes, in the 400–1,100 nm wavelength region, commencing seven days after parturition during 23 days of the lactation period. The NIR spectral dataset included measurements from animals with different physiological conditions—both healthy and mastitic (inflammation of mammary glands). Multiple linear regression (MLR) was used to model log SCC (in the range of 3.78–7.07, i.e., SCC ~ 6.025–11.75 million cells/ml) and yielded a correlation coefficient R = 0.853, a standard error of calibration SEC = 0.377, and a standard error of prediction SEP = 0.370, respectively. These values satisfy the demands for fast screening of SCC as a criterion for mastitis diagnosis in dairy cows. In addition, the correlation between milk constituents and spectra was analyzed to determine their influence. Protein in non-homogenized milk, including the SCC, was strongly correlated with a baseline fluctuation in the 600–700 nm wavelength region. The present results demonstrate that it is possible to determine log SCC levels by use of SW-NIRS in combination with MLR.

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References

  1. Unnerstad HE et al (2009) Microbial aetiology of acute clinical mastitis and agent-specific risk factors. Vet Microbiol 137(1–2):90–97

    Article  Google Scholar 

  2. Persson Y, Nyman AKJ, U (2011) Etiology and antimicrobial susceptibility of udder pathogens from cases of subclinical mastitis in dairy cows in Sweden. Acta Vet Scand 53(1):36

    Article  Google Scholar 

  3. Hogeveen H, Huijps K, Lam TJGM (2011) Economic aspects of mastitis: new developments. N Z Vet J 59(1):16–23

    Article  CAS  Google Scholar 

  4. Halasa T, Huijps K, Østerås O, Hogeveen H (2007) Economic effects of bovine mastitis and mastitis management: a review. Vet Q 29(1):18–31

    Article  CAS  Google Scholar 

  5. Nielsen C, Østergaard S, Emanuelson U, Andersson H, Berglund B, Strandberg E (2010) Economic consequences of mastitis and withdrawal of milk with high somatic cell count in Swedish dairy herds. Animal 4(10):1758–1770

    Article  CAS  Google Scholar 

  6. Urech E, Puhan Z, Schällibaum M (1999) Changes in milk protein fraction as affected by subclinical mastitis. J Dairy Sci 82(11):2402–2411

    Google Scholar 

  7. Rainard P, Foucras G, Boichard D, Rupp R (2018) Invited review: low milk somatic cell count and susceptibility to mastitis. J Dairy Sci 101(8):6703–6714

    Article  CAS  Google Scholar 

  8. International Dairy Federation (2013) Guidelines for the use and interpretation of bovine milk somatic cell count. Bull. IDF 466/2013

    Google Scholar 

  9. Hillerton JE (1999) Redefining mastitis based on somatic cell count. Bull Int Dairy Federation 345:4–6

    Google Scholar 

  10. Smith KL (1995) Standards for somatic cells in milk: physiological and regulatory. IDF Mastit Newsl 144(21):7

    Google Scholar 

  11. Barbano DM, Rasmussen RR, Lynch JM (1991) Influence of milk somatic cell count and milk age on cheese yield. J Dairy Sci 74(2):369–388

    Article  Google Scholar 

  12. Talukder M, Ahmed HM (2017) Effect of somatic cell count on dairy products: a review. Asian J Med Biol Res 3(1):1–9

    Google Scholar 

  13. Sato T et al (1987) Analysis of milk constituents by the near infrared spectrophotometric method. Nihon Chikusan Gakkaiho 58(8):698–706

    Article  CAS  Google Scholar 

  14. Laporte MF, Paquin P (1999) Near-infrared analysis of fat, protein, and casein in cow’s milk

    Google Scholar 

  15. Tsenkova R (1998) Near infrared spectroscopy (NIRS): uniform measuring technology for sustainable dairy production system. J Near Infrared Spectrosc 6(201):A45–A51

    Article  CAS  Google Scholar 

  16. Tsenkova R, Atanassova S, Itoh K, Ozaki Y, Toyoda K (2000) Near infrared spectroscopy for biomonitoring: cow milk composition measurement in a spectral region from 1,100 to 2,400 nanometers. J Anim Sci 78(3):515–522

    Article  CAS  Google Scholar 

  17. Tsenkova R, Atanassova S, Toyoda K, Ozaki Y, Itoh K, Fearn T (1999) Near-infrared spectroscopy for dairy management: measurement of unhomogenized milk composition. J Dairy Sci 82(11):2344–2351

    Article  CAS  Google Scholar 

  18. Šašić S, Ozaki Y (2001) Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least-squares regression and band assignment. Anal Chem 73(1):64–71

    Google Scholar 

  19. Kawamura S, Kawasaki M, Nakatsuji H, Natsuga M (2007) Near-infrared spectroscopic sensing system for online monitoring of milk quality during milking. Sens Instrum Food Qual Saf 1(1):37–43

    Article  Google Scholar 

  20. Kawamura S, Tsukahara M, Natsuga M, Itoh K (2003) On-line near infrared spectroscopic sensing technique for assessing milk quality during milking. Las Vegas, NV, p 1

    Google Scholar 

  21. Iweka P, Kawamura S, Mitani T, Koseki S (2018) Non-destructive online real-time milk quality determination in a milking robot using near-infrared spectroscopic sensing system. Food Suffic AZOJETE 14(SP.i4):121–128

    Google Scholar 

  22. Tsenkova R, et al. (2006) Near infrared spectra of cows’ milk for milk quality evaluation: disease diagnosis and pathogen identification. J Near Infrared Spectrosc 14(6):363–370

    Google Scholar 

  23. Meilina H, Kuroki S, Jinendra BM, Ikuta K, Tsenkova R (2009) Double threshold method for mastitis diagnosis based on NIR spectra of raw milk and chemometrics. Biosyst Eng 104(2):243–249

    Article  Google Scholar 

  24. Tsenkova R, Iordanova KI, Shinde Y (1992) Near infrared spectroscopy for evaluating milk quality. In: Ipema AH (ed) Prospects for automatic milking. Pudoc Scientific Publishers, Wageningen, Netherlands, pp 185–193

    Google Scholar 

  25. Tsenkova R, Atanassova S, Kawano S, Toyoda K (2001) Somatic cell count determination in cow’s milk by near-infrared spectroscopy: a new diagnostic tool. J Anim Sci 79(10):2550–2557

    Article  CAS  Google Scholar 

  26. Kawasaki M, Kawamura S, Tsukahara M, Morita S, Komiya M, Natsuga M (2008) Near-infrared spectroscopic sensing system for on-line milk quality assessment in a milking robot. Comput Electron Agric 63(1):22–27

    Article  Google Scholar 

  27. Iyo C, Terada F, Chen JY, Kawano S (1999) Development of calibration with sample cell compensation for determining the fat content of unhomogenised raw milk by a simple near infrared transmittance method. J Near Infrared Spectrosc 7(4):265–273

    Article  Google Scholar 

  28. Robertson WH, Diken EG, Price EA, Shin JW, Johnson MA (2003) Spectroscopic determination of the OH- solvation shell in the OH-.(H2O)n clusters. Science 299(5611):1367–72

    Google Scholar 

  29. Tsenkova RN, Iordanova IK, Toyoda K, Brown DR (2004) Prion protein fate governed by metal binding. Biochem Biophys Res Commun 325(3):1005–1012

    Article  CAS  Google Scholar 

  30. Osborne BG, Fearn T, Hindle PH (1993) Practical NIR spectroscopy with applications in food and beverage analysis. Longman Singapore Publ. Ltd., Singapore

    Google Scholar 

  31. Chatani E, Tsuchisaka Y, Masuda Y, Tsenkova R (2014) Water molecular system dynamics associated with amyloidogenic nucleation as revealed by real time near infrared spectroscopy and aquaphotomics. PLoS One 9(7):e101997

    Google Scholar 

  32. Tsenkova R (2009) Aquaphotomics: dynamic spectroscopy of aqueous and biological systems describes peculiarities of water. J Near Infrared Spectrosc 17(6):303–313

    Google Scholar 

  33. McGlone VA, Kawano S (1998) Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biol Technol 13(2):131–141

    Article  Google Scholar 

  34. Shin JW, et al (2004) Infrared signature of structures associated with the H+(H2O)n (n=6–27) clusters. Science (80-) 304(5674):1137–1140

    Google Scholar 

  35. Sakudo A et al (2006) Comparison of the vibration mode of metals in HNO3 by a partial least-squares regression analysis of near-infrared spectra. Biosci Biotechnol Biochem 70(7):1578–1583

    Article  CAS  Google Scholar 

  36. Viguier C, Arora S, Gilmartin N, Welbeck K, O’Kennedy R (2009) Mastitis detection: current trends and future perspectives. Trends Biotechnol 27(8):486–493

    Article  CAS  Google Scholar 

  37. Wu D, He Y, Feng S (2008) Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment. Anal Chim Acta 610(2):232–242

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Kobe University, Kobe, Japan

    Roumiana Tsenkova & Jelena Muncan

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  1. Roumiana Tsenkova
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  2. Jelena Muncan
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Correspondence to Roumiana Tsenkova .

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Tsenkova, R., Muncan, J. (2022). Non-destructive Somatic Cell Count Measurement Using Near-Infrared Spectra of Milk in the 400–1,100 nm Short Wavelength Region. In: Aquaphotomics for Bio-diagnostics in Dairy. Springer, Singapore. https://doi.org/10.1007/978-981-16-7114-2_10

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