Luqing Li

2.1k total citations · 1 hit paper
56 papers, 1.7k citations indexed

About

Luqing Li is a scholar working on Analytical Chemistry, Pathology and Forensic Medicine and Biomedical Engineering. According to data from OpenAlex, Luqing Li has authored 56 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Analytical Chemistry, 35 papers in Pathology and Forensic Medicine and 31 papers in Biomedical Engineering. Recurrent topics in Luqing Li's work include Tea Polyphenols and Effects (35 papers), Spectroscopy and Chemometric Analyses (34 papers) and Advanced Chemical Sensor Technologies (31 papers). Luqing Li is often cited by papers focused on Tea Polyphenols and Effects (35 papers), Spectroscopy and Chemometric Analyses (34 papers) and Advanced Chemical Sensor Technologies (31 papers). Luqing Li collaborates with scholars based in China, New Zealand and Hong Kong. Luqing Li's co-authors include Jingming Ning, Zhengzhu Zhang, Yujie Wang, Ying Liu, Qingqing Cui, Quansheng Chen, Wei‐Wei Deng, Menghui Li, Shanshan Jin and Wenjing Huang and has published in prestigious journals such as Angewandte Chemie International Edition, Food Chemistry and Trends in Food Science & Technology.

In The Last Decade

Luqing Li

51 papers receiving 1.6k citations

Hit Papers

Sensomics analysis of the effect of the withering method ... 2022 2026 2023 2024 2022 50 100 150

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Luqing Li China 26 1.0k 789 584 344 253 56 1.7k
Chunwang Dong China 30 875 0.9× 718 0.9× 989 1.7× 718 2.1× 260 1.0× 85 2.0k
Paulo Henrique Gonçalves Dias Diniz Brazil 23 965 1.0× 659 0.8× 129 0.2× 286 0.8× 367 1.5× 70 1.5k
Dabing Ren China 18 460 0.5× 268 0.3× 217 0.4× 291 0.8× 469 1.9× 44 1.2k
Guangxin Ren China 17 521 0.5× 352 0.4× 212 0.4× 110 0.3× 103 0.4× 27 720
Montserrat Mestres Spain 31 1.1k 1.1× 970 1.2× 144 0.2× 1.6k 4.7× 451 1.8× 71 2.9k
Alireza Sanaeifar China 17 594 0.6× 509 0.6× 98 0.2× 140 0.4× 114 0.5× 30 1.0k
Fei Shen China 25 590 0.6× 382 0.5× 50 0.1× 442 1.3× 290 1.1× 71 1.5k
Haibo Yuan China 34 604 0.6× 525 0.7× 2.1k 3.5× 1.7k 5.0× 411 1.6× 106 3.1k
Marc Meurens Belgium 22 848 0.8× 409 0.5× 55 0.1× 269 0.8× 298 1.2× 41 1.6k
H.H. Nieuwoudt South Africa 28 1.0k 1.0× 453 0.6× 68 0.1× 1.2k 3.4× 230 0.9× 78 2.3k

Countries citing papers authored by Luqing Li

Since Specialization
Citations

This map shows the geographic impact of Luqing Li'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 Luqing Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luqing Li more than expected).

Fields of papers citing papers by Luqing Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Luqing Li. 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 Luqing Li. The network helps show where Luqing Li may publish in the future.

Co-authorship network of co-authors of Luqing Li

This figure shows the co-authorship network connecting the top 25 collaborators of Luqing Li. A scholar is included among the top collaborators of Luqing Li 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 Luqing Li. Luqing Li 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, Shengpeng, Clemens Altaner, Feng Lin, et al.. (2025). A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions. Food Research International. 205. 115870–115870. 12 indexed citations
2.
Chen, Yurong, Jing Wang, Yifan Zuo, et al.. (2025). Improvement of near-infrared spectroscopic assessment methods for the quality of Keemun black tea: Utilizing transfer learning. Food Research International. 209. 116184–116184.
3.
He, Daming, Hui Xu, Peng Cao, et al.. (2025). Development of Chiral Bisphosphine Ligands with a Terminal Olefin Enables Asymmetric Orthogonal Auto‐Tandem Catalysis. Angewandte Chemie International Edition. 64(27). e202506881–e202506881. 1 indexed citations
6.
7.
Ning, Jingming, et al.. (2025). Elucidating the taste profile in black tea through molecular docking and molecular sensory science. Food Chemistry. 491. 145104–145104. 1 indexed citations
8.
Lu, Mingxia, Ke Han, Tiehan Li, et al.. (2024). Revealing the differences in aroma of black tea under different drying methods based on GC–MS, GC-O. Food Chemistry X. 23. 101782–101782. 14 indexed citations
9.
Zhang, Jixin, Mengyuan Yang, Siqi Zhang, et al.. (2024). Aroma visualization: A cutting-edge sensor for evaluating the roasting quality of large-leaf yellow tea. LWT. 208. 116684–116684. 1 indexed citations
10.
Cui, Qingqing, Yuming Wei, Li Zou, et al.. (2024). Simultaneous detection of mixed colorants adulterated in black tea based on various morphological SERS sensors. Food Research International. 199. 115364–115364. 3 indexed citations
11.
Li, Luqing, Yurong Chen, Menghui Li, et al.. (2024). E-nose and colorimetric sensor array combining homologous data fusion strategy discriminating the roasting degree of large-leaf yellow tea. Food Chemistry X. 21. 101124–101124. 10 indexed citations
12.
Wei, Yuming, et al.. (2023). Chemical imaging for determining the distributions of quality components during the piling fermentation of Pu-erh tea. Food Control. 158. 110234–110234. 6 indexed citations
13.
Li, Menghui, et al.. (2023). A rapid aroma quantification method: Colorimetric sensor-coupled multidimensional spectroscopy applied to black tea aroma. Talanta. 263. 124622–124622. 19 indexed citations
14.
Huang, Wenjing, Shimao Fang, Jing Wang, et al.. (2022). Sensomics analysis of the effect of the withering method on the aroma components of Keemun black tea. Food Chemistry. 395. 133549–133549. 157 indexed citations breakdown →
15.
Wang, Xiaozhong, et al.. (2021). Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 252. 119522–119522. 41 indexed citations
16.
Li, Luqing, Menghui Li, Qingqing Cui, et al.. (2021). Rapid monitoring of black tea fermentation quality based on a solution-phase sensor array combined with UV-visible spectroscopy. Food Chemistry. 377. 131974–131974. 39 indexed citations
17.
Wang, Yujie, Menghui Li, Luqing Li, Jingming Ning, & Zhengzhu Zhang. (2020). Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer. Food Chemistry. 345. 128816–128816. 81 indexed citations
18.
Li, Luqing, Shanshan Jin, Yujie Wang, et al.. (2020). Potential of smartphone-coupled micro NIR spectroscopy for quality control of green tea. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 247. 119096–119096. 53 indexed citations
19.
Li, Shan, Jian Li, Jun Li, et al.. (2011). Hypoglycemic effects and constituents of the barks of Cyclocarya paliurus and their inhibiting activities to glucosidase and glycogen phosphorylase. Fitoterapia. 82(7). 1081–1085. 55 indexed citations
20.
Li, Luqing, Jun Li, Yan Huang, et al.. (2011). Lignans from the heartwood of Streblus asper and their inhibiting activities to Hepatitis B virus. Fitoterapia. 83(2). 303–309. 38 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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