## 4 Conclusions  
This study confirmed significant differences in aroma between traditional suitable and unsuitable tea cultivars for oolong tea processing based on sensory data, electronic nose data, HS-SPME-GC-MS data and their correlations. Expert review and consumer acceptance evaluation scores were consistent with the suitability classifications. The electronic nose data adequately distinguished the suitability classification, albeit with some blind spots. HS-SPME-GC-MS data and correlation analysis suggested that some volatiles (such as (Z)-Jasmone and \(\beta\)-Ocimene) may affect the sensory differences in tea cultivars of different suitability, and are related to the characteristics of the cultivar, thus being termed as \"Cultivar aroma\". Several other aroma compounds with a high content (such as (E)-Nerolidol and \(\alpha\)-Farnesene) in oolong tea are generated in the processing mode; however, they are weakly related to cultivar suitability and are termed as \"Technical aroma\". Both cultivar aroma and technical aroma may contribute jointly to the aromatic characteristics of oolong tea. This work preliminarily interprets the relationship between the aroma and the suitability of oolong tea, and enlightens the research on the suitability of tea cultivars. The findings provide a theoretical basis for the issue of cultivar selection for global tea production.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods11182880/s1](https://www.mdpi.com/article/10.3390/foods11182880/s1)]([https://www.mdpi.com/article/10.3390/foods11182880/s1](https://www.mdpi.com/article/10.3390/foods11182880/s1)). Supplementary Material S1: Table S1. Expert sensory and consumer acceptance evaluation of six oolong tea samples. Table S2. The variables of the final step in the multiple regression linear model of consumer acceptance and its parameter estimation. Table S3. The optimal multiple linear regression model equation. Table S4. Parameter estimates, probability values and odds ratios of the influence of consumers' purchase intention. Table S5. Stable response values of electronic nose sensors for six oolong tea samples. Table S6. Adonis analysis results of adaptive grouping in PCA analysis of electronic nose data. Table S7. Adonis analysis results of sample grouping in PCoA analysis of HS-SPME-GC-MS data. Table S8. Adonis analysis results of suitability grouping in PCoA analysis of HS-SPME-GC-MS data of different samples Table S9. Relative content of aroma compounds in HS-SPME-GC-MS analysis of six oolong tea samples. Table S10 Spearman correlation coefficient of between electronic nose data, HS-SPME-GC-MS data, and sensory data in six tea cultivars [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69].  
**Author Contributions:** Data curation: C.H. and Y.L.; formal analysis, C.H.; funding acquisition, D.N.; investigation, C.H. and Y.L.; methodology, C.H., J.Z., X.Y. and Y.C.; project administration, D.N.; resources, D.N. and Z.Y.; software, C.H., J.Z., X.Y. and Y.C.; supervision, D.Z. and Z.Y.; writing--original draft, C.H.; writing--review & editing, C.H., D.N. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key R & D Program of China (Project No. 2021 YED1000401) and National Natural Science Foundation of China (U21A20231).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** The data for the consumers were collected and stored in accordance with Huazhong Agricultural University Human Ethics application ID Number: HZAUHU-2020-0017.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** We appreciate the Key Laboratory of Horticulture Plant Biology for the technical support.  
**Conflicts of Interest:** The authors declare no conflict of interest.