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

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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://www.rsdjournal.org/index.php/rsd/article/view/2808. Acesso em: 25 apr. 2024.

Issue

Vol. 9 No. 4

Section

Agrarian and Biological Sciences

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