Chang-Su Na

Biotransformation of major ginsenosides in ginsenoside model culture by lactic acid bacteria

Background Some differences have been reported in the biotransformation of ginsenosides, probably due to the types of materials used such as ginseng, enzymes, and microorganisms. Moreover, most microorganisms used for transforming ginsenosides do not meet food-grade standards. We investigated the statistical conversion rate of major ginsenosides in ginsenosides model culture during fermentation by lactic acid bacteria (LAB) to estimate possible pathways. Methods Ginsenosides standard mix was used as a model culture to facilitate clear identification of the metabolic changes. Changes in eight ginsenosides (Rb1, Rb2, Rc, Rd, Re, Rf, Rg1, and Rg2) during fermentation with six strains of LAB were investigated. Results In most cases, the residual ginsenoside level decreased by 5.9–36.8% compared with the initial ginsenoside level. Ginsenosides Rb1, Rb2, Rc, and Re continuously decreased during fermentation. By contrast, Rd was maintained or slightly increased after 1 d of fermentation. Rg1 and Rg2 reached their lowest values after 1–2 d of fermentation, and then began to increase gradually. The conversion of Rd, Rg1, and Rg2 into smaller deglycosylated forms was more rapid than that of Rd from Rb1, Rb2, and Rc, as well as that of Rg1 and Rg2 from Re during the first 2 d of fermentation with LAB. Conclusion Ginsenosides Rb1, Rb2, Rc, and Re continuously decreased, whereas ginsenosides Rd, Rg1, and Rg2 increased after 1–2 d of fermentation. This study may provide new insights into the metabolism of ginsenosides and can clarify the metabolic changes in ginsenosides biotransformed by LAB.

Metabolite profiling of fermented ginseng extracts by gas chromatography mass spectrometry

Background Ginseng contains many small metabolites such as amino acids, fatty acids, carbohydrates, and ginsenosides. However, little is known about the relationships between microorganisms and metabolites during the entire ginseng fermentation process. We investigated metabolic changes during ginseng fermentation according to the inoculation of food-compatible microorganisms. Methods Gas chromatography mass spectrometry (GC-MS) datasets coupled with the multivariate statistical method for the purpose of latent-information extraction and sample classification were used for the evaluation of ginseng fermentation. Four different starter cultures (Saccharomyces bayanus, Bacillus subtilis, Lactobacillus plantarum, and Leuconostoc mesenteroide) were used for the ginseng extract fermentation. Results The principal component analysis score plot and heat map showed a clear separation between ginseng extracts fermented with S. bayanus and other strains. The highest levels of fructose, maltose, and galactose in the ginseng extracts were found in ginseng extracts fermented with B. subtilis. The levels of succinic acid and malic acid in the ginseng extract fermented with S. bayanus as well as the levels of lactic acid, malonic acid, and hydroxypruvic acid in the ginseng extract fermented with lactic acid bacteria (L. plantarum and L. mesenteroide) were the highest. In the results of taste features analysis using an electronic tongue, the ginseng extracts fermented with lactic acid bacteria were significantly distinguished from other groups by a high index of sour taste probably due to high lactic acid contents. Conclusion These results suggest that a metabolomics approach based on GC-MS can be a useful tool to understand ginseng fermentation and evaluate the fermentative characteristics of starter cultures.

A GC-MS based metabolomics approach to determine the effect of salinity on Kimchi

GC-MS datasets coupled with multivariate statistical analysis were used to investigate metabolic changes in Kimchi during fermentation and metabolic differences in Kimchi added with various amounts (0, 1.25, 2.5, and 5%) of salts. PCA score plot obtained after 1day of fermentation were clearly distinguishable by different salinity groups, implying that early fermentation speed varied according to Kimchi salinity. PLS-DA score plot from data obtained on the 50th day of fermentation also showed a clear separation, indicating metabolites of Kimchi were different according to salinity. Concentrations of lactic acid, acetic acid, and xylitol were the highest in Kimchi with 5% salinity while concentration of fumaric acid was the highest in Kimchi with 0% salinity. Rarefaction curves showed that numbers of operational taxonomic units (OTUs) in Kimchi with 5% salinity were higher than those in Kimchi with 0% salinity, implying that Kimchi with 5% salinity had more bacterial diversities. This study highlights the applicability of GC-MS based metabolomics for evaluating fermentative characteristics of Kimchi with different salinities.