Food Science of Animal Resources (한국축산식품학회지)

Korean Society for Food Science of Animal Resources (한국축산식품학회)
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Development of Rapid Somatic Cell Counting Method by Using Dye Adding NIR Spectroscopy

색소첨가 NIR을 이용한 우유 체세포수 측정법 개발

  • Published : 2008.03.30
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Abstract

To develop the somatic cell counting NIR Spectrum method within a range of 400-2500 nm, eosin-Y, methyl red, methylene blue, resazurin and amido black 10B were tested at 0.01% in raw milk. The PLS (Partial Least Square) results are summarized as follows: Correlation coefficients of the calibration model measurements by NIR spectroscopy in raw milk for eosin-Y, methyl red, methylene blue, resazurin and amido black 10B were 0.78, 0.65, 0.63, 0.65, 0.98 and 0.99, respectively. The correlation coefficients of the prediction model measurements by NIR spectroscopy in raw milk for eosin-Y, methyl red, methylene blue, resazurin and amido black 10B were 0.49, 0.21, 0.36, 0.47, 0.95 and 0.98 respectively. Based on these results, amido black 10B was the best additive for the NIRS somatic cell count method.

본 연구에서는 근적외선 분광광도계를 이용하여 원유의 체세포수 측정에 필요한 최적의 색소를 선정하기 위하여 eosin-Y, methyl red, methylen blue, resazurin 및 amido black 10B등의 5가지 색소를 0.01%첨가 후 근적외선 분광광도계를 이용하여 400-2,500 nm 영역에서 측청하였으며, PLS(partial least squar)분석 결과는 다음과 같다. 교정부 모델에서의 상관도는 raw milk 0.78, eosin-Y 0.65, methyl red 0.63, methylen blue 0.65, resazurin 0.98 그리고 amido black 10B는 0.99 였다. 또한, 검증부 모델에서의 상관도는 raw milk 0.49, eosin-Y 0.21, methyl red 0.36, methylen blue 0.47, resazurin 0.95 그리고 amido black 10B는 0.98 였다. 위 결과를 종합해 보면, amido black 10B를 첨가한 경우 검증부 모델의 상관도는 0.98, 검증부 오차(SEP)는 0.09로 가장 우수한 결과를 보였다.

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References

  1. Ahn, B. S., Kie, K. S., Suh., K. H., Yeo, M. J., Lee, J. M., Jeon, S. B., Park, S. B., and Kim, H. S. (2004) The effect on somatic cell score and milk components by days in holstein dairy cows. Korean J. Anim. Sci. Technol. 46, 925-936 https://doi.org/10.5187/JAST.2004.46.6.925
  2. Brennan. D., Alderman J., Sattler. L., O'Connor. B., and O'Mathuna. C. (2003) Issues in development of NIR micro spectrometer system for on-line process monitoring of milk product. Measurement 33, 67-74 https://doi.org/10.1016/S0263-2241(02)00019-2
  3. Choi C. H., Kim Y J., Kim J. M., and Kim K. S. (2006) Prediction of somatic cell count in raw milk by reflectance spectra. Proceed. Japan-Korea Joint Symp. on Near Infrared Spectroscopy, pp. 250-251
  4. Choi C. H., Kim Y. J., Kim J. H., Nam. E. H., and Kim K. S. (2006) Development of measuring technique for somatic cell count in raw milk by near infrared spectroscopy. Proceed. Korean Biosystems Eng. Conf., Suwon, Korea, 11, 361-365
  5. Christensen, D. A. (1981) Foss electric information, applied cell counting for optimum dairy production. Hillered Botrykkeri. 1, 48
  6. Chun, J. U. and Cho, K. C (2007) Discrimination of Korean and Japanese green tea prducts using NIR spectroscopy. J. Kor. Tea Soc. 13, 93-104
  7. Dohhoo, I. R. and Meek, A. H. (1982) Somatic cell counts in bovine milk. J. Can. Vat. 23, 119-125
  8. Herald, C. W., Jones, G. M., Nickerson, S. C., Patterson, W. N., and Vinson, N. E. (1977) Preliminary evaluation of the fossomatic cell counter for analysis of indiviual cow samples in a centraltesting laboratory. J. Food. Prot. 40, 5223-5226
  9. Heringstad, B., Chang, Y. M., Gialnola, D., and Klemetsdal, G. (2003) Genetic analysis of longitudinal trajectory of clinical mastitis in first-lactation Norwegian cattle. J. Dairy Sci. 76, 2676
  10. Jung, H. I. and Kim, H. J. (2000) Near-infrared spectroscopy: Principles. Anal. Sci. Technol. 13, 1-14
  11. Lund, M. S., Jensen, J., and Pertersen, P. H. (1999) Estimation of genetic and phenotypic parameters for clinical mastitis, somatic cell production deviance, and protein yield in dairy cattle using Gibbs sampling. J. Dairy Sci. 82, 1045 https://doi.org/10.3168/jds.S0022-0302(99)75325-7
  12. Noh M. J., Jeong J. I., Min S. S., Park Y. S., and Kim S. J. (2004) A study on the determination of adulteration of seasame oil by near infrared spectroscopy. Korean J. Food Technol. 36, 527-530.
  13. Poutrel, B. and Lerondelle, C. (1982) Cell content of goat milk: California mastitis test, coulter counter, and fossmatic method for predicting half infection. J. Dairy Sci. 66, 2575-2579 https://doi.org/10.3168/jds.S0022-0302(83)82129-8
  14. Pravdova. V., Walczak. B., Massart. D. L., Kawano. S., Toyoda. K., and Tsenkova. R. (2001) Calibration of somatic cell count in milk based on near-infrared spectroscopy. Anal. Chim. Acta 450, 131-141 https://doi.org/10.1016/S0003-2670(01)01373-3
  15. Purnomoadi, A., Keshab K., Batajoo, Koichiro Ueda, and Fuminori Terada. (1999) Influence of feed source on determination of fat and protein in milk by near-infrared spectroscopy. Int. Dairy J. 9, 447-452 https://doi.org/10.1016/S0958-6946(99)00050-3
  16. Samore, A. S., Schneidder, M. del P., Canvesi, F., Bangnato, A., and Groen, A. F. (2003) Relationship between somatic cell count and functional longevity assessed using survival analysis in Italian Holstein-Friesian cows. Livest. Prod. Sci. 80, 211 https://doi.org/10.1016/S0301-6226(02)00185-9
  17. Tsenkove. R., Atanassove. S., Kawano. R., and 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
  18. Tsenkove. R., Yordanov, K. I., and Shinde, Y. (1992) Nearinfrared spectroscopy for evaluating milk quality. In: Prospects for Atomatic Milking. EAAP Publ. 65, Pudoc, Wageningen, The Netherlands, pp. 185-192
  19. Windholz, M. (1983) The Merck Index 10th ed., Merck & Co., Inc
  20. 김기성, 임상동, 양승용, 신미순, 최창현, 김용주, 김재민 (2006) 근적외선을 이용한 우유 체세포수 검사법 개선 연구 보고서. 한국식품연구원. pp. 8-32

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