# Distinctive Formation of Volatile Compounds in
## 2 Results and Discussion  
### PLS-DA for Volatile Metabolite Profiles in Fermented Rice  
PLS-DA was performed to evaluate the differences among volatile compound profiles obtained from GC-MS data for different fermentation microbes (molds, yeasts, and lactic acid bacteria). The classification of different fermented samples and controls (RICE, non-inoculated and steamed rice), and also discriminant volatile compounds, are shown in Figure 1. The PLS-DA model explained 57.2% of total variance, such as 34.6% PLS [1] and 22.6% PLS [2]. The model was robust and predictable. The parameters of the cross-validation modeling for the third PLS component were R\({}^{2}\)Y = 0.996, and Q\({}^{2}\)Y = 0.997. Permutation testing (50 iterations) was also performed in order to further validate the model, after which R\({}^{2}\) = 0.131 and Q\({}^{2}\) = -0.753. In a score plot of PLS-DA, AOR (fermented by _Aspergillus oryzae_) and ROR (_Rhyzopus oryzae_) were clearly separated from SCR (_Sacharomyces cerevisiae_), SFR (_Sacharomycoposis fibuligera_), LFR (_Lactobacillus fermentum_), LPR (_Lactobacillus plantarum_), and control sample (RICE, non-fermented rice), and SCR, SFR, LFR, and LPR were closely placed near RICE. It can be assumed that _A. oryzae_ and _R. oryzae_ were more active in the changes of volatile compounds during rice fermentation than other microbes. Table 1 lists the distinct volatile compounds from each microbe. A total of 21 significant variables contributing to discriminate samples (\(p\)-value \(>\)0.1 or \(<\)\(-\)0.1) were selected on the basis of a threshold of 1.0 on the variable importance in the projection (VIP) test. Some volatile metabolites derived from amino acids, such as 2-methylbutanal, 3-methylbutanal, ethyl 2-methylbutanoate, 2-methylpropanoic acid, and 2-phenylacetaldehyde, were highly correlated to AOR, while 3-methylbutyl acetate, 5-ethyloxolan-2-one, 3-ethoxypropan-1-ol, ethyl tetradecanoate, 1-phenylethanone, and 2-phenylethanol, which are mainly derived from amino acids and fatty acids, were representative of ROR. In addition, volatile metabolites derived from fatty acids and butanediol fermentation were major discriminant volatile compounds in other samples, such as RICE, SCR, SFR, LFR, and LPR. Figure 1: Partial least squares-discriminant analysis (PLS-DA) score plot for volatile compounds profiles in fermented rice inoculated by different microbes. (**A**) Score plot, (**B**) loading plot. AOR = _Aspergillus oryzae_, ROR = _Rhyzopus oryzae_, SCR = _Sacharomyces cerevisiae_, SFR = _Sacharomycoposis fibuligera_, LFR = _Lactobacillus fermentum_, LPR = _Lactobacillus plantarum_, RICE = controls (non-fermented rice). Table 2 shows the identified volatile compounds with their relative peak areas and retention indices (RI). A total of 57 volatile compounds, such as eight carbohydrate-derived, 20 amino acid-derived, and 29 fatty acid-derived volatile compounds, were found in all samples. During rice fermentation, the generation of volatile compounds varied according to each microbe in this study. In addition, we observed that the total contents of volatile compounds were much higher in AOR and ROR compared to other samples. The major volatile compounds generated by AOR were ethanol, 3-methylbutanan-1-ol, 3-methylbutanal, and 2-methylbutanal, while 3-methylbutan-1-ol, ethanol, and 2-methylpropan-1-ol were the major volatile compounds in ROR. In addition, the contents of ethanol and 3-methylbutan-1-ol were higher than those of other volatile compounds in SCR, whileutan-2,3-diol, butane-2,3-dione, and 3-methyl-butanan-1-ol were the predominant volatile compounds in SFR. The level of 3-hydroxy-2-butanone was the most abundant in LFR. On the other hand, acetic acid and 3-hydroxy-2-butanone were the main metabolites of LPR, but overall formation of volatile compounds was weakly generated compared to other fermented samples. These results indicated that certain volatile compounds were generated at high levels, specific to each microbe.