## 4 Conclusions  
The aroma profiles of eight popular peach cultivars produced in Shanghai were comprehensively analyzed using GC-MS, OAV determination and GCIMS. Seventy VOCs were detected using GC-MS, including nine aldehydes, ten esters, eleven terpenoids, nine alcohols, six lactones, five ketones, five alkanes, eleven aromatic hydrocarbons, two furans and two other compounds, of which twenty-three VOCs were commonly identified in eight peach cultivars. The most VOCs were detected in HJML and the least in JG. Cluster analysis classified XFML, JG, DTML and JXIU into one cluster, which means that their aroma profiles are more similar.  
In addition, based on an OAV \(\geq\) 1, a total of 17 key aroma compounds were screened, and their contributions were ranked as follows: \(\gamma\)- decalactone\(>\)\(\beta\)- ionone \(>\) hexanal \(>\) 2-hexanal \(>\) linalool \(>\) nonanal \(>\) 3-hexenol \(>\) 8-decalactone \(>\) D-limonene \(>\) \(\beta\)-myrce  
Figure 6: Principal component analysis (PCA) score plot (**a**) and PCA loading plot of different peach cultivars (**b**).  
\(>\) 1-hexanol \(>\) \(\gamma\)-octalactone \(>\)\(\gamma\)-dodecalactone \(>\) (Z)-3-hexen-1-yl acetate \(>\) butyl acetate \(>\) isopentyl acetate \(>\) octanal. The OAV of VOCs differed greatly among the cultivars. Lactones had the highest OAV in HJML, which indicates that HJML is the cultivar that contains the most fruity notes. Terpenoids had the highest OAV in JC, meaning that JC had the strongest floral notes. It is obvious that JXIU was the cultivar with the lightest aroma.  
Furthermore, a total of twenty-six VOCs were detected using GC-IMS, among which aldehydes accounted for the largest proportion. GC-IMS has the advantage of data visualization, which can visualize the differences of volatiles among samples. It can be directly observed in the fingerprints that aldehydes have the strongest signals in samples. The PCA results showed that XFML, JG, DTML and JXIU were close to each other, whereas HJML, HH and JC were distant from other samples, which is similar to the results of the cluster analysis. The differences in focus and working conditions between GC-MS and GC-IMS led to significant differences in the composition of the VOCs detected by them. The combination of GC-MS and GC-IMS can capture relatively complete flavor information of samples, which is a hot research topic in the field of flavor at present and will be for a long time in the future.  
Conceptualization, X.L., X.Y. and Z.Y; methodology, X.W. and X.L.; software, X.W.; validation, X.L.; formal analysis, X.W. and X.L.; investigation, X.L.; resources, M.S., M.Z., J.D., H.Z. and X.Z.; data curation, X.W. and Y.H.; writing--original draft preparation, X.W. and X.L.; writing--review and editing, X.L., Z.Y. and X.Y.; visualization, X.W.; supervision, Z.Y. and X.Y.; project administration, X.L. and Z.Y.; funding acquisition, X.L. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by funds from the National Key Research and Development Program of China (2019 YFD1000801), the Shanghai Science and Technology committee Rising-Star Program (19 QB1404600), Huong Ke Chuang Zi-2021-1-1, the Shanghai Municipal Science and Technology Commission 2019 Science and Technology innovation Action Plan Agricultural Project (19391903400), the Outstanding Team Program of Shanghai Academy of Agricultural Science (Grant No. ZY2022-004), the Pangao Plan of Shanghai Academy of Agricultural Science, the Qiankehe Basics [2019]1408 and the Qiankehe Platform Talent [2018]5781.  
Not applicable.  
The authors declare no conflict of interest.