The Use of principal component analysis (PCA) for evaluating results from pig meat quality measurements
References (22)
- et al.
Livest. Prod. Sci.
(1982) - et al.
Meat Sci.
(1988) - et al.
Meat Sci.
(1991) - et al.
Chemometrics and Intelligent Lab. Systems
(1987) Agric. Biol. Chem.
(1979)- et al.
J. Anim. Sci.
(1973)- et al.
Biometrics
(1976) Biometrika
(1971)
Cited by (34)
Relationship between gilt behavior and meat quality using principal component analysis
2014, Meat ScienceCitation Excerpt :The coefficients (loadings) of the eigenvectors for the first three principal components were determined (Karlsson, 1992). The relevance of each variable in each principal component was calculated as the percentage of the absolute value of its loading with respect to the sum of the absolute values of all the loadings in the eigenvector (Karlsson, 1992). Based on the obtained relevance values, we considered a variable as represented enough in the principal component if its relative relevance was above 4.0%.
Influence of dietary fat on pork eating quality
2012, Meat ScienceCitation Excerpt :The relationships among physical and biochemical parameters and sensory quality are of paramount importance to understand how the dietary fats impact on the eating quality. Karlsson (1992) proposed the use of principal component analysis for evaluating meat quality when several correlated measurements are used. This technique is used to find a smaller set of measurements explaining most of the observed variability in the measurements taken, but also helps in examining the relationships among traits and the differences between the groups of animals compared (Hernández, Pla, Oliver, & Blasco, 2000).
Effect of ultimate pH and freezing on the biochemical properties of proteins in turkey breast meat
2011, Food ChemistryCitation Excerpt :Normally, only a few PCs are sufficient to describe the total variation (Smith, 1991). The size of the variation for each component (vector) is indicated by the size of the latent root or eigenvalue (Karlsson, 1992). Furthermore, PCA can give a global representation of the data in a two dimensional plane defined by two components, which can be very useful to group objects with similar characteristics.