The use of principal component analysis to characterise the retail buffalo meat
Author(s): Prajwal S, Vasudevan VN, Sunil B, Sathu T, Anil KS, Shynu M and Sunanda C
Abstract: The principal component analysis (PCA) statistical method was applied to characterise the 80 retail buffalo meat samples (round muscles) in Thrissur district of Kerala, India, analysed for physio-chemical, compositional and bacterial quality at two different intervals of time 7 AM and 1 PM in a day. Coefficient of variance of the 8 variables in the range from 1.81 to 30.68 per cent. PCA transformed the variables into 2 principal components (PC) based on Eigen value, which explain about 74.798 per cent of total variability. PC1 comprise of pH, Warner-Bratzler shear force, R-value, water holding capacity, total viable count and collagen solubility. PC2 was explained by temperature and collagen content. Component loading plot revealed for high correlation for shelf life and the objective measures meat tenderness. The distribution of objects on the axes of the 2 PC’s depicts into two groups, first group had retail buffalo meat analysed at 7 AM and other group 1 PM.
How to cite this article:
Prajwal S, Vasudevan VN, Sunil B, Sathu T, Anil KS, Shynu M, Sunanda C. The use of principal component analysis to characterise the retail buffalo meat. Int J Vet Sci Anim Husbandry 2025;10(1S):24-27.