Using multivariate factor analysis to characterize the unbranched fatty acid profile in bovine rumen fluid

Abstract

Background: Multivariate factor analysis (MFA) could be used for analyzing the complex pattern of correlations among fatty acids (FAs) in the rumen. Objective: To investigate the potential use of MFA to extract information on unbranched FAs metabolism in bovine rumen fluid. Methods: A dataset containing 107 individual records of 26 unbranched FAs from two in vitro ruminal incubation studies was constructed. The MFA was performed using the SPSS (Statistical Package for the Social Sciences) software. Results: The MFA extracted four latent factors, accounting for 86.7% of the total variance. The first factor was positively associated with short (6:0), medium (8:0, 10:0, 12:0, 14:0, and 16:0), and long (17:0, 20:0, and 23:0) saturated FAs (SFAs), as well as with cis and trans monounsaturated FAs (MUFAs) from 14 to 16 carbon atoms (14:1-t5, 14:1-c9, 15:1-t10, 16:1-c9, and 16:1-t9). The second factor was positively correlated with 18:0 and the majority of cis and trans MUFAs of 18 carbon atoms (18:1-t9, 18:1-t11, 18:1-c6, 18:1-c9, and 18:1-c11). The third factor was positively related to 18:3-c9,c12,c15 and 18:2-t11,c15, and negatively to 18:2-c9,t11. The fourth factor was positively correlated with 18:1-t6 and 19:0; however, 19:0 was also negatively associated with the second factor. The 18:2-c9,c12 was negatively correlated with the second and third factors. Conclusion: Multivariate factor analysis (MFA) allowed the reduction of a large number of fatty acids in bovine ruminal fluid to a few latent factors with biological meaning.

Keywords

• cattle
• lipid profile
• lipid metabolism
• multivariate statistics
• multivariate factor analysis
• ruminant
• ruminal fluid
• unbranched fatty acids