SpringerLink
Log in

Near-Infrared Spectroscopy: A New Diagnostic Tool for Determination of Somatic Cell Count

  • Chapter
  • First Online:
Aquaphotomics for Bio-diagnostics in Dairy

Abstract

This chapter will present near-infrared aquaphotomics as a tool for disease diagnosis. Near-infrared (NIR) spectra of non-homogenized milk was acquired in the region 1,100–2,500 nm with the objective to measure somatic cell count (SCC) (Tsenkova et al. in J Anim Sci 79:2550–2557, 2001). Over a period of 28 days, milk from seven Holstein cows was collected starting from the seventh day after calving. The standard milk constituents—fat, protein, lactose and in addition SCC—were analyzed using standard methods. During the experimental period, three of the cows were healthy, while four had periods of mastitis. The calibration for log SCC was performed using partial least squares (PLS) regression analysis. The standard error of calibration was SEC = 0.361, the coefficient of multiple correlation was 0.868, the standard error of prediction (SEP) for an independent validation set of samples was SEP = 0.382. Results achieved in this study demonstrate that NIR spectroscopy can enable screening for mastitis and successful differentiation between milk samples from healthy and mastitic cows. The success in determining accurately the SCC in milk was concluded to be a result of changes in the milk composition, mainly in proteins and ionic content, which influenced the water matrix of the milk and substantially changed the NIR spectra.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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 79:2550–2557

    Article  CAS  Google Scholar 

  2. Damm M, Holm C, Blaabjerg M et al (2017) Differential somatic cell count—a novel method for routine mastitis screening in the frame of dairy herd improvement testing programs. J Dairy Sci 100:4926–4940. https://doi.org/10.3168/jds.2016-12409

    Article  CAS  PubMed  Google Scholar 

  3. Galvan P, Murinda S, Dog LL (2017) Determination of the Prevalence of Major Mastitis-Causing Pathogens in California Dairy Farms Using Polymerase Chain Reaction (PCR). South Calif Conf Undergrad Res

    Google Scholar 

  4. Viguier C, Arora S, Gilmartin N et al (2009) Mastitis detection: current trends and future perspectives. Trends Biotechnol 27:486–493

    Article  CAS  Google Scholar 

  5. Heikkilä AM, Liski E, Pyörälä S, Taponen S (2018) Pathogen-specific production losses in bovine mastitis. J Dairy Sci 101:9493–9504. https://doi.org/10.3168/jds.2018-14824

    Article  CAS  PubMed  Google Scholar 

  6. LeBlanc SJ, Lissemore KD, Kelton DF et al (2006) Major advances in disease prevention in dairy cattle. J Dairy Sci 89:1267–2127. https://doi.org/10.3168/jds.S0022-0302(06)72195-6

    Article  CAS  PubMed  Google Scholar 

  7. Bobbo T, Ruegg PL, Stocco G et al (2017) Associations between pathogen-specific cases of subclinical mastitis and milk yield, quality, protein composition, and cheese-making traits in dairy cows. J Dairy Sci 100:4868–4883. https://doi.org/10.3168/jds.2016-12353

    Article  CAS  PubMed  Google Scholar 

  8. Rainard P, Foucras G, Boichard D, Rupp R (2018) Invited review: low milk somatic cell count and susceptibility to mastitis. J Dairy Sci 101:6703–6714. https://doi.org/10.3168/jds.2018-14593

    Article  CAS  PubMed  Google Scholar 

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

    Google Scholar 

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

    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 74:369–388. https://doi.org/10.3168/JDS.S0022-0302(91)78179-4

    Article  Google Scholar 

  12. Talukder M, Ahmed HM (2017) Effect of somatic cell count on dairy products: a review. Asian J Med Biol Res 3:1–9. https://doi.org/10.3329/ajmbr.v3i1.32030

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. 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 610:232–242. https://doi.org/10.1016/j.aca.2008.01.056

    Article  CAS  PubMed  Google Scholar 

  15. 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 73:64–71. https://doi.org/10.1021/ac000469c

    Article  CAS  PubMed  Google Scholar 

  16. 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 1:37–43. https://doi.org/10.1007/s11694-006-9001-x

    Article  Google Scholar 

  17. Kawamura S, Tsukahara M, Natsuga M, Itoh K (2003) On-line near infrared spectroscopic sensing technique for assessing milk quality during milking. In: 2003 ASAE annual international meeting. american society of agricultural and biological engineers, Las Vegas, Nevada, pp 1–10

    Google Scholar 

  18. Tsenkova R, Itoh K, Himoto J, Asahida K (1995) Near infrared spectroscopy analysis of unhomogenized milk for automated monitoring in dairy husbandry. In: Butten GD, Flinn PC, Welsh LA, Blakeney AB (eds) Lea** ahead with near infrared spectroscopy. Royal Australian Chemical Institute, pp 329–333

    Google Scholar 

  19. Laporte MF, Paquin P (1999) Near-infrared analysis of fat, protein, and casein in cow’s milk. J Agric Food Chem 47:2600–2605. https://doi.org/10.1021/JF980929R

    Article  CAS  PubMed  Google Scholar 

  20. 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 

  21. Kawasaki M, Kawamura S, Tsukahara M et al (2008) Near-infrared spectroscopic sensing system for on-line milk quality assessment in a milking robot. Comput Electron Agric 63:22–27. https://doi.org/10.1016/J.COMPAG.2008.01.006

    Article  Google Scholar 

  22. Rinnan Å, van den Berg F, Engelsen SB (2009) Review of the most common pre-processing techniques for near-infrared spectra. TrAC—Trends Anal. Chem. 28:1201–1222

    Article  CAS  Google Scholar 

  23. Savitzky A, Golay MJE (1951) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639

    Article  Google Scholar 

  24. Martens H, Martens M (2001) Multivariate analysis of quality: an introduction. Wiley, Chichester UK

    Google Scholar 

  25. Steel RGD, Torrie JH (1980) Principles and procedures of statistics, a biometrical approach. McGraw-Hill Kogakusha, Ltd.

    Google Scholar 

  26. Whyte D, Claycomb, R. Kunnemeyer R (2000) Measurement of somatic cell count, fat and protein in milk using visible to near infra-red spectroscopy. In: 2000 asae annual international meeting. Milwaukee, Wisconsin, USA

    Google Scholar 

  27. 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 14:121–128

    Google Scholar 

  28. Harmon RJ (1994) Physiology of mastitis and factors affecting somatic cell counts. J Dairy Sci 77:2103–2112. https://doi.org/10.3168/jds.S0022-0302(94)77153-8

    Article  CAS  PubMed  Google Scholar 

  29. Franca M, Del Valle TA, Campana M et al (2017) Mastitis causative agents and SCC relationship with milk yield and composition in dairy cows. Arch Zootech 66:45–49

    Article  CAS  Google Scholar 

  30. Bagri DK, Kumari R, Bagdi DL, et al (2018) Effect of subclinical mastitis on milk composition in lactating cows. ~ 231 ~ J Entomol Zool Stud 6

    Google Scholar 

  31. Bannerman DD, Paape MJ, Baldwin VIRL et al (2006) Effect of mastitis on milk perchlorate concentrations in dairy cows. J Dairy Sci 89:3011–3019. https://doi.org/10.3168/jds.S0022-0302(06)72574-7

    Article  CAS  PubMed  Google Scholar 

  32. Poutrel B, Caffin JP, Rainard P (1983) Physiological and pathological factors influencing bovine serum albumin content of milk. J Dairy Sci 66:535–541. https://doi.org/10.3168/JDS.S0022-0302(83)81822-0

    Article  CAS  PubMed  Google Scholar 

  33. Verdi RJ, Barbano DM, Dellavalle ME, Senyk GF (1987) Variability in true protein, casein, nonprotein nitrogen, and proteolysis in high and low somatic cell milks. J Dairy Sci 70:230–242. https://doi.org/10.3168/JDS.S0022-0302(87)80002-4

    Article  CAS  PubMed  Google Scholar 

  34. Urech E, Puhan Z, Schällibaum M (1999) Changes in milk protein fraction as affected by subclinical mastitis. J Dairy Sci 82:2402–2411. https://doi.org/10.3168/jds.S0022-0302(99)75491-3

    Article  CAS  PubMed  Google Scholar 

  35. Szczubiał M, Dąbrowski R, Kankofer M et al (2008) Concentration of serum amyloid a and activity of ceruloplasmin in milk from cows with clinical and subclinical mastitis. Bull Vet Inst Pulawy 52:391–395

    Google Scholar 

  36. Lippolis JD, Reinhardt TA (2005) Proteomic survey of bovine neutrophils. Vet Immunol Immunopathol 103:53–65. https://doi.org/10.1016/j.vetimm.2004.08.019

    Article  CAS  PubMed  Google Scholar 

  37. Maeda H, Ozaki Y, Tanaka M et al (1995) Near infrared spectroscopy and chemometrics studies of temperature-dependent spectral variations of water: relationship between spectral changes and hydrogen bonds. J Near Infrared Spectrosc 3:191–201. https://doi.org/10.1255/jnirs.69

    Article  CAS  Google Scholar 

  38. Tsenkova R (2009) Aquaphotomics: dynamic spectroscopy of aqueous and biological systems describes peculiarities of water. J Near Infrared Spectrosc 17:303–313. https://doi.org/10.1255/jnirs.869

    Article  CAS  Google Scholar 

  39. Kojić D, Tsenkova R, Tomobe K et al (2014) Water confined in the local field of ions. ChemPhysChem 15:4077–4086. https://doi.org/10.1002/cphc.201402381

    Article  CAS  PubMed  Google Scholar 

  40. Gowen AA, Tsenkova R, Esquerre C et al (2009) Use of near infrared hyperspectral imaging to identify water matrix co-ordinates in mushrooms (Agaricus bisporus) subjected to mechanical vibration. J Near Infrared Spectrosc 17:363–371. https://doi.org/10.1255/jnirs.860

    Article  CAS  Google Scholar 

  41. Sahu S, Nanavati S, Tomar SS, et al (2018) Association between Somatic Cell Count, Electric Conductivity and pH in Diagnosis of Subclinical Mastitis in Crossbred Cows. INDIAN J Vet Sci Biotechnol 13. https://doi.org/10.21887/ijvsbt.v13i03.10618

  42. Molt K, Berentsen S, Frost VJ, Niemoller A (1998) NIR spectrometry-an alternative for the analysis of aqueous systems. J Near Infrared Spectrosc 10:16–21

    CAS  Google Scholar 

  43. Binette JF, Buijs H (1996) Fourier transform near infrared process monitoring of multiple inorganic ions in aqueous solution. In: Davies AM, Williams P (eds) Near infrared spectroscopy: the future waves. NIR Publications, Charlton, UK, pp 286–289

    Google Scholar 

  44. Weyer LG, Lo S-C (2006) Spectra-structure correlations in the near-infrared. In: Griffiths PR (ed) Handbook of vibrational spectroscopy. John Wiley & Sons Ltd, Chichester, UK

    Google Scholar 

  45. Lee KA (2006) On-line analysis in food engineering. In: Ozaki Y, McClure WF, Christy AA (eds) Near-infrared spectroscopy in food science and technology. John Wiley & Sons Inc., Hoboken, NJ, USA, pp 361–378

    Google Scholar 

  46. Shenk JS (1992) Application of NIR spectroscopy to agricultural products. In: Burns D, Ciurczak E (eds) Handbook of near-infrared analysis. Marcel Dekker, New York, USA, pp 385–386

    Google Scholar 

  47. Bázár G, Kovács Z, Tanaka M, et al (2014) Aquaphotomics and its extended water mirror concept explain why NIRS can measure low concentration aqueous solutions. In: Aquaphotomics, “Understanding Water in Biological World”, The 5th Kobe University Brussels European centre symposium innovation, environment, and globalisation. Brussels, Belgium, pp 215–216

    Google Scholar 

  48. Tsenkova R (2007) Aquaphotomics: extended water mirror approach reveals peculiarities of prion protein alloforms. NIR news 18:14–17

    Google Scholar 

  49. Muncan J, Tsenkova R (2019) Aquaphotomics-from innovative knowledge to integrative platform in science and technology. Molecules 24:2742. https://doi.org/10.3390/molecules24152742

    Article  CAS  PubMed Central  Google Scholar 

  50. van de Kraats EB, Munćan J, Tsenkova RN (2019) Aquaphotomics—origin, concept, applications and future perspectives. Substantia 3(2):13–28. https://doi.org/10.13128/substantia-702

  51. Ng-Kwai-Hang KF, Hayes JF, Moxley JE, Monardes HG (1985) Percentages of protein and nonprotein nitrogen with varying fat and somatic cells in Bovine milk. J Dairy Sci 68:1257–1262. https://doi.org/10.3168/jds.S0022-0302(85)80954-1

    Article  CAS  PubMed  Google Scholar 

  52. Nudda A, Atzori AS, Correddu F, et al (2019) Effects of nutrition on main components of sheep milk. Small Rumin Res 106015. https://doi.org/10.1016/j.smallrumres.2019.11.001

  53. Díaz-Carrillo E, Munoz-Serrano A, Alonso-Moraga A, Serradilla-Manrique JM (1993) Near infrared calibrations for goat`s milk components: protein, total casein, αs, β and κ-casein, fat and lactose. J Near Infrared Spectrosc 1:141–146

    Article  Google Scholar 

  54. Czarnik-Matusewicz B, Murayama K, Tsenkova R, Ozaki Y (1999) Analysis of near-infrared spectra of complicated biological fluids by two-dimensional correlation spectroscopy: protein and fat concentration-dependent spectral changes of milk. Appl Spectrosc 53:1582–1594. https://doi.org/10.1366/0003702991946046

    Article  CAS  Google Scholar 

  55. Wilson RH, Nadeau KP, Jaworski FB et al (2015) Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization. J Biomed Opt 20:030901. https://doi.org/10.1117/1.jbo.20.3.030901

    Article  PubMed  PubMed Central  Google Scholar 

  56. Zhou GX, Ge Z, Dorwart J et al (2003) Determination and differentiation of surface and bound water in drug substances by near infrared spectroscopy. J Pharm Sci 92:1058–1065. https://doi.org/10.1002/jps.10375

    Article  CAS  PubMed  Google Scholar 

  57. Popescu CM, Hill CAS, Popescu MC (2016) Water adsorption in acetylated birch wood evaluated through near infrared spectroscopy. Int Wood Prod J 7:61–65. https://doi.org/10.1080/20426445.2016.1160538

    Article  Google Scholar 

  58. Manley M, du Toit G, Geladi P (2011) Tracking diffusion of conditioning water in single wheat kernels of different hardnesses by near infrared hyperspectral imaging. Anal Chim Acta 686:64–75. https://doi.org/10.1016/j.aca.2010.11.042

    Article  CAS  PubMed  Google Scholar 

  59. Jørgensen AC, Airaksinen S, Karjalainen M et al (2004) Role of excipients in hydrate formation kinetics of theophylline in wet masses studied by near-infrared spectroscopy. Eur J Pharm Sci 23:99–104. https://doi.org/10.1016/j.ejps.2004.06.001

    Article  CAS  PubMed  Google Scholar 

  60. Cervera MF, Karjalainen M, Airaksinen S et al (2004) Physical stability and moisture sorption of aqueous chitosan-amylose starch films plasticized with polyols. Eur J Pharm Biopharm 58:69–76. https://doi.org/10.1016/j.ejpb.2004.03.015

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kobe University, Kobe, Japan

    Roumiana Tsenkova & Jelena Muncan

Authors
  1. Roumiana Tsenkova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jelena Muncan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roumiana Tsenkova .

Rights and permissions

Reprints and permissions

Copyright information

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

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tsenkova, R., Muncan, J. (2022). Near-Infrared Spectroscopy: A New Diagnostic Tool for Determination of Somatic Cell Count. In: Aquaphotomics for Bio-diagnostics in Dairy. Springer, Singapore. https://doi.org/10.1007/978-981-16-7114-2_9

Download citation

Publish with us

Policies and ethics

Search

Navigation