OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning applications for identify the geographical origin, variety and processing of black tea using 1H NMR chemical fingerprinting
Chuanjian Cui, Yifan Xu, Ge Jin, et al.
Food Control (2023) Vol. 148, pp. 109686-109686
Closed Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Metabolomics and chemometrics: The next-generation analytical toolkit for the evaluation of food quality and authenticity
Pascual García-Pérez, Pier Paolo Becchi, Leilei Zhang, et al.
Trends in Food Science & Technology (2024) Vol. 147, pp. 104481-104481
Open Access | Times Cited: 17

A comprehensive systematic review and bibliometric analysis of food fraud from a global perspective
Richard Kwasi Bannor, Kingsley Kofi Arthur, Dixon Oppong, et al.
Journal of Agriculture and Food Research (2023) Vol. 14, pp. 100686-100686
Open Access | Times Cited: 29

Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification
Y.F. Li, Natasha Logan, Brian Quinn, et al.
Food Chemistry (2023) Vol. 438, pp. 138029-138029
Open Access | Times Cited: 22

Comprehensive applications of metabolomics on tea science and technology: Opportunities, hurdles, and perspectives
Mingchun Wen, Mengting Zhu, Zisheng Han, et al.
Comprehensive Reviews in Food Science and Food Safety (2023) Vol. 22, Iss. 6, pp. 4890-4924
Open Access | Times Cited: 19

The classification, detection and ‘SMART’ control of the nine sins of tea fraud
Y.F. Li, Christopher T. Elliott, Awanwee Petchkongkaew, et al.
Trends in Food Science & Technology (2024) Vol. 149, pp. 104565-104565
Open Access | Times Cited: 5

Surface-enhanced Raman spectroscopy-based metabolomics for the discrimination of Keemun black teas coupled with chemometrics
Yin‐feng Ren, Zhi-hao Ye, Xiaoqian Liu, et al.
LWT (2023) Vol. 181, pp. 114742-114742
Open Access | Times Cited: 9

Exploring authenticity and potential markers of Lushan Yunwu green tea by fusing 1H NMR and UPLC-Q/TOF-MS techniques
Xiaoli Yan, Yuting Wang, Xinjin Yu, et al.
Food Control (2024) Vol. 163, pp. 110492-110492
Closed Access | Times Cited: 2

The utilization of a data fusion approach to investigate fingerprint profiles of dark tea from China's different altitudes
Zhenhong Wang, Yuanxi Han, Liyou Zhang, et al.
Food Chemistry X (2024) Vol. 22, pp. 101447-101447
Open Access | Times Cited: 2

Isolation and identification of active components from Grifola frondosa and its anti‐EV71 virus effect
Wenyu Xiong, Xiaoqin Jiang, Junqiang He, et al.
Journal of the Science of Food and Agriculture (2024) Vol. 104, Iss. 7, pp. 4453-4464
Closed Access | Times Cited: 1

Authentication of Laying Hen Housing Systems Based on Egg Yolk Using 1H NMR Spectroscopy and Machine Learning
Greta Bischof, Edwin Januschewski, Andreas Juadjur
Foods (2024) Vol. 13, Iss. 7, pp. 1098-1098
Open Access | Times Cited: 1

1H NMR Spectroscopy Combined with Machine-Learning Algorithm for Origin Recognition of Chinese Famous Green Tea Longjing Tea
Zhiwei Hou, Yugu Jin, Zhe Gu, et al.
Foods (2024) Vol. 13, Iss. 17, pp. 2702-2702
Open Access | Times Cited: 1

Nuclear Magnetic Resonance and Artificial Intelligence
Stefan Kühn, Rômulo Pereira de Jesus, Ricardo M. Borges
Encyclopedia (2024) Vol. 4, Iss. 4, pp. 1568-1580
Open Access | Times Cited: 1

Geographical verification of Pleuropterus multiflorus thunb. by functional compounds, stable isotope ratios, and multielement combined with machine learning methods
Feng Xiong, Siman Wang, Chuan‐Zhi Kang, et al.
Food Chemistry (2023) Vol. 440, pp. 138209-138209
Closed Access | Times Cited: 4

Advancements in the application of carbon nanotubes for tea quality and safety assessment
Li Fu, W.B. Ying
Fullerenes Nanotubes and Carbon Nanostructures (2023) Vol. 31, Iss. 11, pp. 1007-1018
Closed Access | Times Cited: 2

Spectroscopy and machine learning in food processing survey
Mahtem Teweldemedhin Mengstu, Alper Taner, Hüseyin Duran
BIO Web of Conferences (2024) Vol. 85, pp. 01022-01022
Open Access

Machine learning-enabled fatty acid quantification and classification of pork from autochthonous breeds using low-field 1H NMR spectroscopic data.
José Luis Ramiro, Ana G. Neo, Trinidad Pérez‐Palacios, et al.
Food Control (2024) Vol. 166, pp. 110753-110753
Open Access

Advanced analytical techniques for bioactive compounds in tea
Yunle Huang, Rui Min Vivian Goh, Aileen Pua, et al.
Elsevier eBooks (2024), pp. 887-897
Closed Access

Enhanced food authenticity control using machine learning-assisted elemental analysis
Ying Yang, Lu Zhang, Xinquan Qu, et al.
Food Research International (2024) Vol. 198, pp. 115330-115330
Closed Access

Detection of Sugar Syrups in Honey Using Untargeted Liquid Chromatography–Mass Spectrometry and Chemometrics
Jule Hansen, Christof Kunert, Kurt-Peter Raezke, et al.
Metabolites (2024) Vol. 14, Iss. 11, pp. 633-633
Open Access

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