All analyses were carried out at least in triplicate.
Experimental data were analyzed by the analysis of variance (ANOVA),
and the significant differences among means were determined by Duncan’s
multiple range test. Correlation analysis and principal component analysis
(PCA) of the results were performed using SAS 9.1 (SAS Institute, Cary,
NC). PCA was performed on the basis of the correlation matrix and the
calculated eigenvalues and eigenvectors. The principal components with
eigenvalues >1.0 were extracted. The output from PCA consisted of score
plots to visualize the contrast between different samples and loading plots
to explain the reason for cluster separation. Pearson correlation analysis
was carried out among the contents of eight metabolites.