Evaluation and Discrimination of Soy Sauce by Computer Analysis of Volatile Profiles

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Abstract
The relationships between gas chromatographic (GC) profiles and sensory data of 72 purely fermented soy sauce samples were analyzed by multiple regression analysis and principal component analysis (PCA). Prior to the analysis, GC data was transformed into 7 different modes in order to compare the fitting to a hypothetical linear model. The result from logarithmically transformed ratio of each peak to the sum of whole peaks showed the best precision of predictability for sensory score (R = 0.978). As the result of PCA, eigen values of 10 PCs were shown to be larger than 1.0 but the 5 major PCs could account for 66% of the variance in the total variance of 39 GC peaks. The first and second PCs showed great importance for aroma quality and similarity or dissimilarity in profiles of extracted PCs showed a similar trend with quality differences evaluated by sensory tests. These results showed the importance of the harmonious balance of each aroma compound to create a preferable soy sauce aroma.

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