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The Milklipidometric Model: A Mathematical Equation-based on Electric Conductivity to Predict Butterfat Concentrations in Bovine Milk

S. Muyambo, J.A. Urombo, M. Mudyiwa, A. Musengi

Abstract


The milklipidometric model is a mathematical equation to predict butterfat in raw milk and it is based on electric conductivity principle. The model differs from the existing equations in that it uses variables (electric conductivity,  and volume fraction of whole milk ) which can be easily measured and it predicts fat at wide temperature,  range of 5–60°C. Accurate results can be obtained even using commercial electrical conductivity meters, hence making the method easy and cheap, especially for small and medium dairy farmers in developing countries. The model is effective for butterfat concentration between 0.60 and 5.50%. The model has a correlation of determination R2 of 0.890 and root mean square error (RMSE) of 0.464. Validation of the model using Deming regression, Passing and Bablok regression and Bland and Altman method indicate that the equation is capable of reproducing the butterfat from the validation data (N=32) at 95% confidence interval. Further analysis of the model accuracy and precision showed that the equation has a percentage bias of less than 2.9%, a standard deviation of 0.63% and coefficient of variance of 19.19%.

 

Keywords: Electric conductivity, mathematical modelling, Butterfat determination.

Cite this Article

S. Muyambo, J.A. Urombo, M. Mudyiwa, A. Musengi. The Milklipidometric Model: A Mathematical Equation-based on Electric Conductivity to Predict Butterfat Concentrations in Bovine Milk. Research & Reviews: Journal of Dairy Science and Technology. 2019; 8(1): 10–21p.


Keywords


Electric conductivity; mathematical modelling; Butterfat determination

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References


Ethiopian Standard, ES. Milk and milk products-Determination of fat content General guidance on the use of butyrometric methods, ISO 11870:2012. 2012.

Walstra P, Wouters JTM, Geurts TJ. Dairy Science and Technology. 2nd Edn. Boca Raton: Taylor & Francis Group, LLC; 2006.

Food Safety and Standard Authority of India, FSSAI. Manual for analysis of foods: Milk and Milk products. 2012.

Jha SN, Nasaiah K, Basedlya AL, et al. Measurement techniques and applications of electrical properties for non-destructive quality evaluation of foods-a review. J Food Sci. Technol. 2011; 48(4): 387-411p. https://doi.org/10.1007/s13197-011-0263-x

Venkatesh MS, Raghavan GSV. An overview of dielectric properties measuring techniques. Canadian Biosyst. Eng. 2005; 47: 7.15-7.30p.

Mabrook MF, Petty MC. Effect of composition on the electrical conductance of milk. J. Food Eng. 2003; 60: 321–32p. https://doi.org/10.1016/S0260-8774(03)00054-2

Binnur K, Serap K. The effects of temperature and milk fat content on the electrical conductivity of Kefir during incubation. American J. Food Sci. Technol. 2016; 4(1): 25-28p.

Rendevski S, Alkhanbouli ASA, Shaabi KSM et al. Testing for added water in milk with handheld LCR meter. IOSR-JESTFT. 2017; 11(5): 23-30p. https://doi.org/10.9790/2402-1105012330

Henningsson M, Ostergren K, Dejmek P. The electrical conductivity of milk-the effect of dilution and temperature. Int. J Food Prop. 2005; 8: 15-22p. https://doi.org/10.1081/JFP-200048143

Henningsson M, Östergren K, Sundberg R, et al. Sensor fusion as a tool to dynamic dairy processing. Department of Food Technology, Engineering and Nutrition, Lund University, Lund. 2004.

Goodling RC, Rogers GW, Cooper JB, et al. Heritability estimates for electrical conductivity of milk and correlations with predicted transmitting abilities for somatic cell scores. J. Dairy Sci. 2000; 83.

Ilie LI, Tudor L, Galis AM. The electrical conductivity of cattle milk and possibility of mastitis diagnosis in Romania. Faculty of Veterinary Medicine Bucharest Splaiul Independent. 2010.

Walstra P, Geurts TJ, Noomen A, et al. Dairy processing technology: Principles of milk properties and process. Marcel Dekker, Inc. 1999. https://doi.org/10.1201/9780824746414

Ninfa AJ, Ballou DP, Benore M. Fundamental laboratory approaches for biochemistry and biotechnology. 2nd Edn. Hoboken, USA: John Wiley and Sons, Inc. 2010.

Fernandez-Martin, F, Sanz PD. Influence of temperature and composition of some physical properties of milk and milk concentrates: electrical conductivity. Int. Agrophys. 1984; 1(1): 41-54p.

McSweeney PLH, Fox PF. Advanced Dairy Chemistry: Lactose, Water, Salts and Minor constituencies. 3rd Edn. New York: Springer Science + Business Media, LLC. 2009.

Prentice JH. The conductivity of milk -the effect of the volume and degree of dispersion of the fat. J Dairy Res. 1962; 29: 131-139p. https://doi.org/10.1017/S0022029900017738

Lawton BA, Pethig R. Determining the fat content of milk and cream using ac conductivity measurements. Meas. Sci. Technol. 1993; 4: 38–41p. https://doi.org/10.1088/0957-0233/4/1/007

Sundheendranath CS, Rao MB. The relationship between relative viscosity and electrical conductivity of skim-milk. Brief 18th International Dairy Congress, 1E, 89. 1970.

International Dairy Federation, IDF. Analytical quality assurance and good laboratory practices in dairy laboratory. Proceedings of an international seminar (AOAC International, CEC, IDF, VDM); 18-20 May 1992; Sonthofen –Germany. Brussels: IDF. 1992.

International Organisation of Legal Metrology, (OIML R 56). Standard solutions reproducing the conductivity of electrolytes. , OIML R 56 Edition 1981 (E). 1981.

Muyambo S, Urombo JA. Shelf quality studies: modelling of the flow quality and lactic acid bacteria-bifidobacteria quantity, as parameters for monitoring shelf quality of stirred yogurt using shelf time, pH, Bostwick consistency and temperature. IRJBB. 2018; 8(1): 1-12p. http:/dx.doi.org/10.14303/irjbb.2017.078.

Bilić-Zulle L. Comparison of methods: Passing and Bablok regression. Biochem. Med (Zagreb). 2011; 21(1): 49-52. https://doi.org/10.11613/BM.2011.010

Moriasi DN, Arnold JG, van Liew MW, et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. ASABE. 2007; 50(3): 885-900p.

Gupta HV, Sorooshian S, Yapo PO. Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. J Hydrol. Eng. 1999; 4(2): 135-143. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135)

Hyndman, R. J. Measuring forecast accuracy. In: Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice. OTexts. http://otexts.com/fpp. 2012.

Singh J, Knapp HV, Demissie M. Hydrologic modelling of the Iroquois River watershed using HSPF and SWAT. ISWS CR 2004-08. Champaign, Ill.: Illinois State Water Survey. 2004. Available at: www.sws.uiuc.edu/pubdoc/CR/ISWSCR2004-08.pdf. Accessed 8 September 2005.

Walther BA Moore JL. The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography. 2005; 28: 815-829p. https://doi.org/10.1111/j.2005.0906-7590.04112.x

Gomes FP. Curso de estatística experimental. São Paulo: Nobel, 1985; 467p.

Vaz MAB, Pacheco PS, Seidel EJ, et al. Classification of the coefficient of variation to variables in beef cattle experiments. Ciênc. Rural. 2017; 47(11): 1-4. https://doi.org/10.1590/0103-8478cr20160946


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