## 4 Conclusions  
In this investigation, GC-TOF/MS and GC\(\times\)GC-TOF/MS were used to analyze the VOCs of three brands of coffee under three conditions (bean, powder and brew). The total number of VOCs identified by G was less than 100, but the total number of VOCs identified by GG was between 175 and 374. The main types of different compounds were ketones, alcohols, esters, hydrocarbons, aldehydes, sulfur-containing compounds, furans, and pyrazines. After grinding, the number of aldehydes, ketones, alcohols, and sulfur compounds increased. After brewing, the number of VOCs in coffee decreased. The Venndiagram of VOCs showed that the common compounds in the beans, powder, and brews were only 6, 16, and 15, respectively, and the VOCs were different among the three states. The PCA results of the VOCs identified by G clearly showed that different coffee samples could be clearly distinguished by state rather than brand. Compared with the results of G, GG could detect more VOCs, thus promoting the further separation of different brands of coffee. Generally, pyridine, pyrrole, alcohols, and phenols greatly contributed to the distinction of the different coffee states. These compounds were only present in one or two different states or with significant differences in content, such as 1-(2-furan methyl)-1H pyrrole with its hazelnut and coffee aroma, 2-furan methanol with its caramel and tobacco flavor, and 2-methoxy phenol with its meat flavor. Additionally, alcohols greatly contributed to the distinction among the three brands of coffee, such as 2-furan methanol acetate with its fruity flavor. The results of GG showed that the separation of the different brands of coffee was more obvious, especially in the two states of powder and brew. In other words, GG was more conducive to the distinction between some brands in terms of coffee powder and coffee brew in a short time. In conclusion, compared with GC-TOF/MS, GC\(\times\)GC-TOF/MS with its better shape quality and higher peak purity increased the accuracy of the qualitative and quantitative data, which could efficiently detect more VOCs, including those at trace level that played a role in coffee flavor. The technology is suitable for the further identification and differentiation of the different brands of coffee samples.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods12163123/s1](https://www.mdpi.com/article/10.3390/foods12163123/s1)]([https://www.mdpi.com/article/10.3390/foods12163123/s1](https://www.mdpi.com/article/10.3390/foods12163123/s1)).  
X.F., investigation, methodology, writing-original draft preparation; Y.C., writing-original draft preparation; writing--review and editing; J.G., investigation; Z.R., investigation; H.C., methodology, investigation; R.S., investigation; Y.L. ([PERSON]), writing-original draft preparation, funding acquisition; H.Z., writing--review and editing; Y.L. ([PERSON]), supervising. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by High Level Innovation Team and Distinguished Scholar Project of Guangxi Universities and Colleges, 2020[6].  
The data presented in this study are available on reasonable request from the corresponding author.  
We are grateful to Instrument Analysis Center of Shanghai Jiao Tong University for the support of GC-TOF/MS and GC\(\times\)GC-TOF/MS analysis.  
The authors declare no conflict of interest.