The Classification of Body Measurement on Syrian Hamster (Mesocricetus auratus) Based on Factor Analysis and Principal Component Analysis

Abstract

Mesocritus (M.) auratus (Syrian hamster) is not only used a laboratory animal especially in medical research, but also is looked after as a pet animal. Genetic information can be obtained by studying body morphometry of Syrian hamster, and quantitative characteristics can be useful for conservation and breeding development. Body size characteristics of Syrian hamster is becoming the focus of this study. It is, therefore, this study is conducted to classify Syrian hamster into large, medium and small body sizes on the basis of factor analysis (FA) and principal component analysis (PCA). Hotelling T2-statistic showed differences in linear measurement of body surface between male and female Syrian hamsters (P<0.01). Results of FA showed that variable affecting the body size of male M. auratus in this study was body length; however, it did not influence the body size of female significantly. Body length in male hamsters, therefore, can be used as a reference in a selection program based on body size measurements that are not directly related to body weight. Body length is also found as discriminatory variable for body size, and the chest width was the discriminatory variable for body shape for both male and female. The distribution of colors that occur in each body size class indicated that M. auratus still has a variety in coat colors. Results of clustered diagram show that the crowd in body shape of male is greatly different from that of female Syrian hamsters. Classification the male and female Syrian hamster can be performed based on the value of first factor scores (SF-1) for practical purposes, as well as the body size. Both male and female Syrian hamster can be classified into large, medium and small groups, and each hamster individual data have SF-1 score and it own body score. It is concluded that classification of body size in M. auratus based on factor analysis and principal component analysis showed the same results.