Hualing Wu

882 total citations
23 papers, 577 citations indexed

About

Hualing Wu is a scholar working on Pathology and Forensic Medicine, Food Science and Biochemistry. According to data from OpenAlex, Hualing Wu has authored 23 papers receiving a total of 577 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Pathology and Forensic Medicine, 10 papers in Food Science and 9 papers in Biochemistry. Recurrent topics in Hualing Wu's work include Tea Polyphenols and Effects (17 papers), Phytochemicals and Antioxidant Activities (9 papers) and Fermentation and Sensory Analysis (6 papers). Hualing Wu is often cited by papers focused on Tea Polyphenols and Effects (17 papers), Phytochemicals and Antioxidant Activities (9 papers) and Fermentation and Sensory Analysis (6 papers). Hualing Wu collaborates with scholars based in China, United States and Canada. Hualing Wu's co-authors include Dong Chen, Xiaohui Jiang, Kaixing Fang, Hongjian Li, Qing Wang, Qiushuang Wang, Bo Li, Zhongjian Chen, Qian Kong and Shijuan Yan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Experimental Botany and Frontiers in Plant Science.

In The Last Decade

Hualing Wu

22 papers receiving 567 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Hualing Wu China 14 322 232 198 165 139 23 577
Yun Sun China 17 507 1.6× 390 1.7× 184 0.9× 98 0.6× 222 1.6× 39 692
Lan-Sook Lee South Korea 7 239 0.7× 229 1.0× 101 0.5× 84 0.5× 183 1.3× 13 494
I. Sarath B. Abeysinghe Sri Lanka 11 231 0.7× 134 0.6× 284 1.4× 134 0.8× 203 1.5× 20 554
Pengcheng Zheng China 13 394 1.2× 323 1.4× 110 0.6× 90 0.5× 186 1.3× 31 566
Bum-Jin Lee South Korea 7 292 0.9× 189 0.8× 234 1.2× 80 0.5× 125 0.9× 8 497
Jianyong Zhang China 11 149 0.5× 126 0.5× 177 0.9× 129 0.8× 111 0.8× 28 499
Geun‐Seoup Song South Korea 9 63 0.2× 231 1.0× 77 0.4× 175 1.1× 181 1.3× 45 510
Carolina Thomaz dos Santos D’Almeida Brazil 9 288 0.9× 211 0.9× 102 0.5× 63 0.4× 89 0.6× 18 503
Chun Zou China 13 391 1.2× 294 1.3× 196 1.0× 41 0.2× 174 1.3× 22 682
Hyun‐Il Jun South Korea 10 46 0.1× 242 1.0× 87 0.4× 202 1.2× 197 1.4× 30 526

Countries citing papers authored by Hualing Wu

Since Specialization
Citations

This map shows the geographic impact of Hualing Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hualing Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hualing Wu more than expected).

Fields of papers citing papers by Hualing Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hualing Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hualing Wu. The network helps show where Hualing Wu may publish in the future.

Co-authorship network of co-authors of Hualing Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Hualing Wu. A scholar is included among the top collaborators of Hualing Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hualing Wu. Hualing Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wang, Tianqi, Erdong Ni, Shuyue Li, et al.. (2025). Belowground Interaction in Tea/Soybean Intercropping Enhances Tea Quality by Improving Soil Nutrient Dynamics. Plants. 14(11). 1691–1691. 1 indexed citations
2.
Wu, Hualing, Erdong Ni, Kaixing Fang, et al.. (2024). CsRAB, a R2R3-MYB transcription factor from purple tea (Camellia sinensis), positively regulates anthocyanin biosynthesis. Frontiers in Plant Science. 15. 1514631–1514631. 1 indexed citations
3.
Wang, Qiushuang, Xiaohui Jiang, Kaixing Fang, et al.. (2023). Identification of key volatile and odor-active compounds in 10 main fragrance types of Fenghuang Dancong tea using HS-SPME/GC-MS combined with multivariate analysis. Food Research International. 173(Pt 1). 113356–113356. 21 indexed citations
4.
Wang, Qiushuang, Xiaohui Jiang, Kaixing Fang, et al.. (2023). Characterization of the Aroma Profiles of Guangdong Black Teas Using Non-Targeted Metabolomics. Foods. 12(7). 1560–1560. 13 indexed citations
5.
Li, Wenjin, et al.. (2022). Identification of Key Aroma Components in Fuliang Black Tea Based on HS-SPME-GC-MS and OAV. SHILAP Revista de lepidopterología. 1 indexed citations
6.
Wang, Qing, et al.. (2022). Research on the Suitability of Teas Made from Fresh Tea Leaves of Purple Tea. SHILAP Revista de lepidopterología. 2 indexed citations
7.
Jiang, Xiaohui, Kaixing Fang, Qing Wang, et al.. (2022). Identification and characterization of the key volatile flavor compounds in black teas from distinct regions worldwide. Journal of Food Science. 87(8). 3433–3446. 29 indexed citations
8.
9.
Fang, Kaixing, Zhiqiang Xia, Hongjian Li, et al.. (2021). Genome-wide association analysis identified molecular markers associated with important tea flavor-related metabolites. Horticulture Research. 8(1). 42–42. 63 indexed citations
10.
Li, Hongjian, Kaixing Fang, Xiaohui Jiang, et al.. (2021). Comparative Transcriptome Analysis Reveals Putative Genes Responsible for High Theacrine Content in Kucha (Camellia kucha (Chang et Wang) Chang). Tropical Plant Biology. 14(1). 82–92. 4 indexed citations
11.
Mei, Xin, et al.. (2021). A Comparative Metabolomic Analysis Reveals Difference Manufacture Suitability in “Yinghong 9” and “Huangyu” Teas (Camellia sinensis). Frontiers in Plant Science. 12. 767724–767724. 16 indexed citations
12.
Chen, Xiaobing, Yi Zhang, Zhenghua Du, et al.. (2020). Establishing a quantitative volatile measurement method in tea by integrating sample extraction method optimizations and data calibration. Flavour and Fragrance Journal. 36(1). 64–74. 10 indexed citations
13.
Wang, Qiushuang, Hongjian Li, Xiaohui Jiang, et al.. (2020). Identification of key metabolites based on non-targeted metabolomics and chemometrics analyses provides insights into bitterness in Kucha [Camellia kucha (Chang et Wang) Chang]. Food Research International. 138(Pt B). 109789–109789. 56 indexed citations
14.
Wang, Zhi‐Hui, et al.. (2020). Identification of volatile components and analysis of aroma characteristics of Jiangxi Congou black tea. International Journal of Food Properties. 23(1). 2160–2173. 21 indexed citations
15.
Wu, Hualing, Wenjie Huang, Zhongjian Chen, et al.. (2019). GC–MS-based metabolomic study reveals dynamic changes of chemical compositions during black tea processing. Food Research International. 120. 330–338. 106 indexed citations
16.
Chen, Dong, Qianwen Zhang, Xiaohui Jiang, et al.. (2019). Volatile components and nutritional qualities of Viscum articulatum Burm.f. parasitic on ancient tea trees. Food Science & Nutrition. 7(9). 3017–3029. 15 indexed citations
17.
Cheng, Kai, Yingchao Xu, Chao Yang, et al.. (2019). Histone tales: lysine methylation, a protagonist in Arabidopsis development. Journal of Experimental Botany. 71(3). 793–807. 60 indexed citations
18.
Chen, Dong, et al.. (2017). Effects of Pu-erh ripened tea on hyperuricemic mice studied by serum metabolomics. Journal of Chromatography B. 1068-1069. 149–156. 21 indexed citations
19.
20.
Luo, Chun Huai, et al.. (2015). Development of EST-SSR and TRAP markers from transcriptome sequencing data of the mango. Genetics and Molecular Research. 14(3). 7914–7919. 9 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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