Use of multivariate statistics to predict the physicochemical quality of milk

Authors

DOI:

https://doi.org/10.33448/rsd-v9i4.2808

Keywords:

Methods; Principal components; Producers; Animal origin matrix.

Abstract

Multivariate analysis involves the application of statistical and computational methods to predict responses. Among the various methods of statistical analysis multivariate, the analysis by main components is highlighted to predict the composition and quality of food in general. The objective of this work was to characterize the milk producers of the municipality of Itapetinga-BA, using principal component analysis. Twenty samples of raw milk were used, collected at the reception of the dairy located in Itapetinga-BA. The variables analyzed were: fat, density, defatted dry extract, protein and lactose. The first two main components explained 87.24% of the total variation. It was verified the formation of different groups distributed in the four quadrants of the system. First quadrant stood out from the others by forming a group composed of ten producers in the analyzed region, characterized by presenting samples with higher lactose content and lower fat content in milk. The lactose and fat variables are of greater importance in the characterization of milk.

Author Biography

Clara Mariana Gonçalves Lima, Federal University of Lavras

Federal University of Lavras

References

Abbring, S., Hols, G., Garssen, J., & van Esch, B. C. (2019). Raw cow’s milk consumption and allergic diseases–the potential role of bioactive whey proteins. European journal of pharmacology, 843, 55-65.

Cattell, R. B. (1996). “The screen test for the number of factors”. Multivariate behavioral research, 1(2), 245-276.

Conceição, D. G. (2018). Utilização do FTIR aliado à análise quimiométrica como ferramenta de triagem para identificação de adulterantes no leite cru (Masters dissertation, UNIVERSIDADE ESTADUAL DO SUDOESTE DA BAHIA).

D'Auria, E., Mameli, C., Piras, C., Cococcioni, L., Urbani, A., Zuccotti, G. V., & Roncada, P. (2018). Precision medicine in cow's milk allergy: proteomics perspectives from allergens to patients. Journal of proteomics, 188, 173-180.

Guetouache, M., Guessas, B., & Medjekal, S. (2014). Composition and nutritional value of raw milk. Journal Issues ISSN, 2350, 1588.

Lucey, J. A., Otter, D., & Horne, D. S. (2017). A 100-year review: Progress on the chemistry of milk and its components. Journal of Dairy Science, 100(12), 9916-9932.

Maqsood, S., Al-Dowaila, A., Mudgil, P., Kamal, H., Jobe, B., & Hassan, H. M. (2019). Comparative characterization of protein and lipid fractions from camel and cow milk, their functionality, antioxidant and antihypertensive properties upon simulated gastro-intestinal digestion. Food chemistry, 279, 328-338.

Meena, S., Rajput, Y. S., Sharma, R., & Singh, R. (2019). Effect of goat and camel milk vis a vis cow milk on cholesterol homeostasis in hypercholesterolemic rats. Small Ruminant Research, 171, 8-12.

Pereira, P. C. (2014). Milk nutritional composition and its role in human health. Nutrition, 30(6), 619-627.

Schmidt, A., Schreiner, M. G., & Mayer, H. K. (2017). Rapid determination of the various native forms of vitamin B6 and B2 in cow’s milk using ultra-high performance liquid chromatography. Journal of Chromatography A, 1500, 89-95.

Souza, A. M. D., & Poppi, R. J. (2012). Experimento didático de quimiometria para análise exploratória de óleos vegetais comestíveis por espectroscopia no infravermelho médio e análise de componentes principais: um tutorial, parte I. Química Nova, 35(1), 223-229.

Viana, C. C. R. (2018). Caracterização de fórmulas infantis para lactantes usando espectroscopia no infravermelho médio. (Masters dissertation, UNIVERSIDADE FEDERAL DE JUIZ DE FORA).

Zhao, L., Du, M., Gao, J., Zhan, B., & Mao, X. (2019). Label-free quantitative proteomic analysis of milk fat globule membrane proteins of yak and cow and identification of proteins associated with glucose and lipid metabolism. Food Chemistry, 275, 59-68.

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Published

21/03/2020

How to Cite

PINHEIRO, L. O.; JÚNIOR, M. R.; LIMA, C. M. G.; SOUSA, H. C.; PAGNOSSA, J. P.; SANTOS, L. S.; FERNANDES, S. A. de A. Use of multivariate statistics to predict the physicochemical quality of milk. Research, Society and Development, [S. l.], v. 9, n. 4, p. e41942808, 2020. DOI: 10.33448/rsd-v9i4.2808. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/2808. Acesso em: 12 nov. 2024.

Issue

Vol. 9 No. 4

Section

Agrarian and Biological Sciences

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