Aplicando Análisis de Componentes Principales en la Composición de Alimentos para Rumiantes

Fabricio Javier  Rivadeneira Zambrano; Rodolfo Andrés  Rivadeneira Zambrano; Silvia Mercedes Carvajal Rivadeneira; Viviana Katiuska García Macías

doi:10.56124/encriptar.v7i14.008

Authors

Fabricio Javier Rivadeneira Zambrano Universidad Laica Eloy Alfaro de Manabí ULEAM
Rodolfo Andrés Rivadeneira Zambrano Universidad Técnica de Manabí UTM
Silvia Mercedes Carvajal Rivadeneira Unidad Educativa Julio Pierregrosse UEJP
Viviana Katiuska García Macías Universidad Laica Eloy Alfaro de Manabí ULEAM

DOI:

https://doi.org/10.56124/encriptar.v7i14.008

Keywords:

food composition, principal component analysis, dimension reduction

Abstract

The following paper presents the application of the Principal Component Analysis (PCA) method used to analyze quantitative variables for dimension reduction by decomposing the correlation matrix into its eigenvectors and eigenvalues, although other decomposition methods such as SVD (Singular Value Decomposition) can be used.

This method is applied to data relating to the nutritional composition of 150 foods or ingredients for ruminants. The composition of these foods analyzed in the laboratory forms a table of 12 variables or columns, of which 8 are quantitative variables used in the PCA analysis, which represent the main nutrients needed by ruminants, such as: percentage of Dry Matter, Dry Matter Digestibility, Crude Protein, percentage of Rumen Degradable Protein, Neutral Detergent Fiber, percentage of Fiber, Calcium, Phosphorus and Metabolic Energy. The result is a reduction in the size of the feed composition table and four main axes or components are identified as important nutrient factors that affect the quality of feed for ruminants.

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