Use of multivariate statistics to predict the physicochemical quality of milk

• Autor(a) principal: Pinheiro, Lenara Oliveira
• Data de Publicação: 2020
• Outros Autores: Júnior, Mário Roberto, Lima, Clara Mariana Gonçalves, Sousa, Heliara Caires, Pagnossa, Jorge Pamplona, Santos, Leandro Soares, Fernandes, Sérgio Augusto de Albuquerque
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/2808
• topic: Métodos; Componentes principais; Produtores; Matriz de origem animal.; Métodos; Componentes principales; Productores; Matriz de origen animal.; Methods; Principal components; Producers; Animal origin matrix.

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.