## 5 Materials and Methods
### Statistical Analysis  
The data matrices were introduced into SIMCA-P+ 13.0 (Umetrics, Umea, Sweden) software for PCA. Prior to PCA, all variables obtained were mean-centered and scaled to the pareto variance. PCA is an unsupervised technique that reduces the dimensionality of the original data matrix while retaining the maximum amount of variability. Therefore, PCA can explain the differences between distilled liquors by means of factors obtained from the datasets and, simultaneously, determine which variables contribute most to such differences. Correlative network was visualized with Gephi (Version 0.9.1; available at: [[https://gephi.org](https://gephi.org)]([https://gephi.org](https://gephi.org))) [57] using the classic Fruchterman-Reingold algorithm. Before plotting in a correlative network, pairwise Pearson's correlations between variables were computed using SPSS Version 22 (SPSS Inc., Chicago, IL, USA) and the significant correlations (FDR < 0.05) were retained. The FDR value was calculated by R (Version 3.2.2) with the fdrttool package.  
The following are available online at [[http://www.mdpi.com/2218-1989/9/1/2/s1](http://www.mdpi.com/2218-1989/9/1/2/s1)]([http://www.mdpi.com/2218-1989/9/1/2/s1](http://www.mdpi.com/2218-1989/9/1/2/s1)), Table S1: Volatile compounds and their relative concentration (%) in 24 distilled liquors detected by HS-SPME-GC-TOFMS, Table S2: Representative VOCs and their concentration (mg/L) in distilled liquors mentioned in the main text, Table S3: Non-volatile compounds and their relative concentration (%) in 24 distilled liquors detected by GC-TOFMS, Table S4: Representative Non-VOCs and their concentration (mg/L) in distilled liquors mentioned in the main text, Table S5: Compounds involved in Figure 4, Figure S1: Heating showing the clustering of the samples, Figure S2: PCA loadings of the (A) VOCs and (B) non-VOCs, Figure S3: Venn diagrams extract the common compounds from each kind of distilled liquor, Figure S4: The PCA plot of QCs and samples demonstrate the stability and the repeatability of the equipment.  
Conceptualization, W.J. and Y.X.; Data curation, C.F.; Funding acquisition, Y.X.; Investigation, H.D.; Project administration, W.J.; Supervision, H.D., W.J., and Y.X.; Visualization, C.F.; Writing--original draft, C.F.; Writing--review and editing, W.J. and Y.X.  
This work was supported by the National Natural Science Foundation of China (NSFC) (grants 31530055, 31501469, and 21506074), the Natural Science Foundation of Jiangsu Province of China (grant BK20150143), the National Key RandD R&D Program (no. 2016 YFD0400503) and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (grant KYCX17_1418).  
We would like to thank [PERSON] and [PERSON] from Shanghai Jiao Tong University Affiliated Sixth People's Hospital for their technical assistance. They helped perform the formal analysis and provided software support. Special thanks also to the Institute for Chinese Soy Sauce Aroma Type Liquor, Beijing Advanced Innovation Center for Food Nutrition and Human Health and Chinese Liquor Industrial Technology Innovation Strategic Alliance for their financial support.  
The authors declare no conflict of interest.