A principal component analysis required in technical assistance guidance for chilled raw milk producers

• Autor(a) principal: Leal, Dyhogo Henrique Veloso
• Data de Publicação: 2022
• Outros Autores: Azevedo, Alcinei Mistico, Almeida, Anna Christina de, Pires Neto, Otaviano de Souza, Duarte, Eduardo Robson, Raidan, Fernanda Santos Silva
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Acta Scientiarum. Animal Sciences (Online)
• Texto Completo: https://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/55570
• topic: dairy cattle; milk quality; multivariate analyses; PCA.; dairy cattle; milk quality; multivariate analyses; PCA.

The purpose of the present study was to evaluate the principal component analysis (PCA) to guide technical assistance regarding several dairy farms’ issues, which includes improving microbiological quality and physical-chemical composition of raw refrigerated milk. Data of monthly analysis of fat, protein, lactose, dry defatted stratum, somatic cell count, total bacterial count, milk temperature of 8,101 samples of milk from expansion tanks and production of 78 farms located in the northern region of Minas Gerais, Brazil were processed. Descriptive statistical measures and Pearson correlation coefficient were estimated involving all evaluated traits during the dry and rainy seasons. In addition, multivariate analyses were performed using PCA. The results showed that two farm sites were negatively related to milk quality in both seasons. One farm stood out positively, being able to be used as a herd management model to drive technical assistance actions. Thus, PCA is efficient in simplifying large amounts of data, allowing simpler and faster technical herd management interpretation.