Data on physicochemical analysis (pH, total acidity, and the
contents of total soluble solids, residual sugar, ethanol and volatile
flavor compounds) and microbiological enumeration (fungi, yeasts
and bacteria) of the resulting soy sauce were statistically analyzed
using one-way ANOVA procedure with SPSS ver. 16.0 software
(SPSS, Chicago, IL, USA). Significant differences were determined
using Duncan's multiple range test at the level of p < 0.05. Principal
component analysis (PCA) with varimax rotation was used to
reduce the dimensionality of the data set to a small number of PCs
with high correlation. PCA bi-plots of major principal components
were drawn to demonstrate the differences and similarities among
the samples. Hierarchical cluster analysis (HCA) with Euclidean
distance and average between-groups linkage was also used to
classify the fermented soy sauce samples into subgroups that had
similar physicochemical properties.