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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:

Enhanced quality monitoring during black tea processing by the fusion of NIRS and computer vision
Yujie Wang, Luqing Li, Ying Liu, et al.
Journal of Food Engineering (2021) Vol. 304, pp. 110599-110599
Closed Access | Times Cited: 60

Showing 1-25 of 60 citing articles:

A Review of Machine Learning for Near-Infrared Spectroscopy
Wen‐Wen Zhang, Liyanaarachchi Chamara Kasun, Qi Jie Wang, et al.
Sensors (2022) Vol. 22, Iss. 24, pp. 9764-9764
Open Access | Times Cited: 76
Monitoring chlorophyll changes during Tencha processing using portable near-infrared spectroscopy
Lihua Liu, Muhammad Zareef, Zhen Wang, et al.
Food Chemistry (2023) Vol. 412, pp. 135505-135505
Open Access | Times Cited: 43
Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives
Minqiang Guo, Kaiqiang Wang, Hong Lin, et al.
Comprehensive Reviews in Food Science and Food Safety (2024) Vol. 23, Iss. 1
Closed Access | Times Cited: 23
Rapid identification of adulterated rice based on data fusion of near-infrared spectroscopy and machine vision
Chenxuan Song, Jinming Liu, Chunqi Wang, et al.
Journal of Food Measurement & Characterization (2024) Vol. 18, Iss. 5, pp. 3881-3892
Closed Access | Times Cited: 15
Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology
Zhongyuan Liu, Rentian Zhang, Chongshan Yang, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2022) Vol. 271, pp. 120921-120921
Closed Access | Times Cited: 63
Monitoring black tea fermentation quality by intelligent sensors: Comparison of image, e-nose and data fusion
Qiaoyi Zhou, Zhenhua Dai, Feihu Song, et al.
Food Bioscience (2023) Vol. 52, pp. 102454-102454
Closed Access | Times Cited: 26
Data fusion strategy for rapid prediction of moisture content during drying of black tea based on micro-NIR spectroscopy and machine vision
Xufeng Sheng, Jiezhong Zan, Yongwen Jiang, et al.
Optik (2023) Vol. 276, pp. 170645-170645
Closed Access | Times Cited: 22
Predictive models for sensory score and physicochemical composition of Yuezhou Longjing tea using near-infrared spectroscopy and data fusion
Yong Chen, Mengqi Guo, Kai Chen, et al.
Talanta (2024) Vol. 273, pp. 125892-125892
Closed Access | Times Cited: 10
Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics
Ying Liu, Junlan Huang, Menghui Li, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2021) Vol. 267, pp. 120537-120537
Closed Access | Times Cited: 42
Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions
Yue Zhao, Jia Li
Environmental Science & Technology (2022) Vol. 56, Iss. 24, pp. 17531-17544
Closed Access | Times Cited: 29
Determination of Tibetan tea quality by hyperspectral imaging technology and multivariate analysis
Chunyi Zhan, Peng Huang, Yuchao Wang, et al.
Journal of Food Composition and Analysis (2023) Vol. 117, pp. 105136-105136
Closed Access | Times Cited: 19
Spectroscopy and computer vision techniques for noninvasive analysis of legumes: A review
Shaojin Ma, Yongyu Li, Yankun Peng
Computers and Electronics in Agriculture (2023) Vol. 206, pp. 107695-107695
Closed Access | Times Cited: 19
Vision-based fruit recognition via multi-scale attention CNN
Weiqing Min, Zhiling Wang, Jiahao Yang, et al.
Computers and Electronics in Agriculture (2023) Vol. 210, pp. 107911-107911
Closed Access | Times Cited: 18
Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects
Yanying Zhang, Yuanzhong Wang
Food Chemistry X (2023) Vol. 19, pp. 100860-100860
Open Access | Times Cited: 18
Hyperspectral Imaging and Machine Learning as a Nondestructive Method for Proso Millet Seed Detection and Classification
Nader Ekramirad, Lauren E. Doyle, Julia R. Loeb, et al.
Foods (2024) Vol. 13, Iss. 9, pp. 1330-1330
Open Access | Times Cited: 5
Taste evaluation by NIR spectroscopy and blending strategy of black tea based on GDE3 algorithm
Caijin Ling, Lin Huang, Yingnan Bian, et al.
Infrared Physics & Technology (2025), pp. 105739-105739
Closed Access
Rapid and real time detection of black tea rolling quality by using an inexpensive machine vison system
Shuai Shen, Na Ren, Hang Zheng, et al.
Food Research International (2025) Vol. 205, pp. 115983-115983
Closed Access
A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions
Shengpeng Wang, Clemens Altaner, Feng Lin, et al.
Food Research International (2025) Vol. 205, pp. 115870-115870
Closed Access
Investigating Research Trends in Computer Vision for Food Quality Control: A Bibliometric Approach
Natalya Lysova, Federico Solari, Roberto Montanari
Procedia Computer Science (2025) Vol. 253, pp. 1923-1932
Open Access
Visualizing chemical indicators: Spatial and temporal quality formation and distribution during black tea fermentation
Yujie Wang, Zhengyu Ren, Yuyu Chen, et al.
Food Chemistry (2022) Vol. 401, pp. 134090-134090
Closed Access | Times Cited: 26
1H NMR-based metabolomics combined with chemometrics to detect edible oil adulteration in huajiao (Zanthoxylum bungeanum Maxim.)
Chuanjian Cui, Mingyue Xia, Libin Chen, et al.
Food Chemistry (2023) Vol. 423, pp. 136305-136305
Closed Access | Times Cited: 14
Accurate and rapid quantification of tea saponins using a thin-layer chromatography (TLC) method based on hemolysis and machine vision
Chuanjian Cui, Yanshuo Wu, Xiaoxiang Guo, et al.
LWT (2024) Vol. 199, pp. 116139-116139
Open Access | Times Cited: 4
Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning
Chunyi Zhan, Lijia Xu, Peng Huang, et al.
Agriculture (2021) Vol. 11, Iss. 11, pp. 1106-1106
Open Access | Times Cited: 29
pH indicator-based sensor array in combination with hyperspectral imaging for intelligent evaluation of withering degree during processing of black tea
Yujie Wang, Zhengyu Ren, Maoyu Li, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2022) Vol. 271, pp. 120959-120959
Closed Access | Times Cited: 21
Non-destructive identification of moldy walnut based on NIR
Minhui An, Chengmao Cao, Shishun Wang, et al.
Journal of Food Composition and Analysis (2023) Vol. 121, pp. 105407-105407
Closed Access | Times Cited: 11
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