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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:

Utilizing near infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch
Duangkamolrat Khamsopha, Woranitta Sahachairungrueng, Sontisuk Teerachaichayut
Food Control (2020) Vol. 123, pp. 107781-107781
Closed Access | Times Cited: 54

Showing 1-25 of 54 citing articles:

Deep learning hyperspectral imaging: a rapid and reliable alternative to conventional techniques in the testing of food quality and safety
Naillah Gul, Khalid Muzaffar, Syed Zubair Ahmad Shah, et al.
Quality Assurance and Safety of Crops & Foods (2024) Vol. 16, Iss. 1, pp. 78-97
Open Access | Times Cited: 19
Determination of hardness for maize kernels based on hyperspectral imaging
Mengmeng Qiao, Yang Xu, Guoyi Xia, et al.
Food Chemistry (2021) Vol. 366, pp. 130559-130559
Closed Access | Times Cited: 69
Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review
Shun Zhang, Shuliang Liu, Li Shen, et al.
Current Research in Food Science (2022) Vol. 5, pp. 1305-1312
Open Access | Times Cited: 51
Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain
Zhilong Kang, Yuchen Zhao, Lei Chen, et al.
Food Engineering Reviews (2022) Vol. 14, Iss. 4, pp. 596-616
Open Access | Times Cited: 39
Quantitative detection of metanil yellow adulteration in chickpea flour using line-scan near-infrared hyperspectral imaging with partial least square regression and one-dimensional convolutional neural network
Dhritiman Saha, T. Senthilkumar, C. B. Singh, et al.
Journal of Food Composition and Analysis (2023) Vol. 120, pp. 105290-105290
Closed Access | Times Cited: 30
Authenticity Discrimination and Adulteration Level Detection of Camellia Seed Oil via Hyperspectral Imaging Technology
Yuqian Shang, Liwei Bao, Haiwen Bi, et al.
Food Analytical Methods (2024) Vol. 17, Iss. 3, pp. 450-463
Closed Access | Times Cited: 9
Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis
Brajesh Kumar Panda, Gayatri Mishra, Wilmer Ariza Ramirez, et al.
Journal of Food Engineering (2021) Vol. 318, pp. 110889-110889
Closed Access | Times Cited: 46
Rapid determination of TBARS content by hyperspectral imaging for evaluating lipid oxidation in mutton
Naiyun Fan, Xiang Ma, Guishan Liu, et al.
Journal of Food Composition and Analysis (2021) Vol. 103, pp. 104110-104110
Closed Access | Times Cited: 41
Rapid identification of lamb freshness grades using visible and near-infrared spectroscopy (Vis-NIR)
Jingjing Zhang, Guishan Liu, Yan Li, et al.
Journal of Food Composition and Analysis (2022) Vol. 111, pp. 104590-104590
Closed Access | Times Cited: 31
Evaluation of aroma quality using multidimensional olfactory information during black tea fermentation
Ting An, Yang Li, Xi Tian, et al.
Sensors and Actuators B Chemical (2022) Vol. 371, pp. 132518-132518
Closed Access | Times Cited: 28
Fusion of spectra and texture data of hyperspectral imaging for prediction of myoglobin content in nitrite-cured mutton
Guoling Wan, Shuxiang Fan, Guishan Liu, et al.
Food Control (2022) Vol. 144, pp. 109332-109332
Closed Access | Times Cited: 28
Classification and adulteration of mengding mountain green tea varieties based on fluorescence hyperspectral image method
Zhiyong Zou, Qingsong Wu, Long Tao, et al.
Journal of Food Composition and Analysis (2023) Vol. 117, pp. 105141-105141
Closed Access | Times Cited: 22
A Review of the Use of Near-Infrared Hyperspectral Imaging (NIR-HSI) Techniques for the Non-Destructive Quality Assessment of Root and Tuber Crops
Michael Adesokan, Emmanuel Oladeji Alamu, Bolanle Otegbayo, et al.
Applied Sciences (2023) Vol. 13, Iss. 9, pp. 5226-5226
Open Access | Times Cited: 16
Ability of visible imaging and machine learning in detection of chickpea flour adulterant in original cinnamon and pepper powders
Mohammad Hossein Nargesi, Kamran Kheiralipour
Heliyon (2024) Vol. 10, Iss. 16, pp. e35944-e35944
Open Access | Times Cited: 7
Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat
Iman Tahmasbian, Natalie K. Morgan, Shahla Hosseini Bai, et al.
Remote Sensing (2021) Vol. 13, Iss. 6, pp. 1128-1128
Open Access | Times Cited: 39
Quantitative prediction and visual detection of the moisture content of withering leaves in black tea (Camellia sinensis) with hyperspectral image
Chunwang Dong, Ting An, Ming Yang, et al.
Infrared Physics & Technology (2022) Vol. 123, pp. 104118-104118
Closed Access | Times Cited: 24
Geographical origin discrimination of lemon myrtle (Backhousia citriodora) leaf powder using near-infrared hyperspectral imaging
Maral Seididamyeh, Iman Tahmasbian, Anh Dao Thi Phan, et al.
Food Bioscience (2024) Vol. 59, pp. 103946-103946
Open Access | Times Cited: 5
Near-infrared spectroscopy combined with support vector machine for the identification of Tartary buckwheat (Fagopyrum tataricum (L.) Gaertn) adulteration using wavelength selection algorithms
Yue Yu, Yinghui Chai, Yujie Yan, et al.
Food Chemistry (2024) Vol. 463, pp. 141548-141548
Closed Access | Times Cited: 5
Contaminant detection in pistachio nuts by different classification methods applied to short-wave infrared hyperspectral images
Giuseppe Bonifazi, Giuseppe Capobianco, Riccardo Gasbarrone, et al.
Food Control (2021) Vol. 130, pp. 108202-108202
Closed Access | Times Cited: 31
Assessing the Levels of Robusta and Arabica in Roasted Ground Coffee Using NIR Hyperspectral Imaging and FTIR Spectroscopy
Woranitta Sahachairungrueng, Chanyanuch Meechan, Nutchaya Veerachat, et al.
Foods (2022) Vol. 11, Iss. 19, pp. 3122-3122
Open Access | Times Cited: 21
Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm
Binbin Fan, Rongguang Zhu, Dongyu He, et al.
Foods (2022) Vol. 11, Iss. 15, pp. 2278-2278
Open Access | Times Cited: 20
Research on Rapid and Non-Destructive Detection of Coffee Powder Adulteration Based on Portable Near-Infrared Spectroscopy Technology
Fujie Zhang, Xiaoning Yu, Lixia Li, et al.
Foods (2025) Vol. 14, Iss. 3, pp. 536-536
Open Access
Near-infrared hyperspectral imaging for predicting the quality of SO2 pre-treated and dehydrated mango
Wayan Dipasasri Aozora, Achiraya Tantinantrakun, Anthony Keith Thompson, et al.
Journal of Food Science and Technology (2025)
Closed Access
Origin traceability of agricultural products: A lightweight collaborative neural network for spectral information processing
Yan Shi, Yang Yu, Jinyue Zhang, et al.
Food Research International (2025) Vol. 208, pp. 116131-116131
Closed Access
Spectroscopic techniques combined with chemometrics for rapid detection of food adulteration: Applications, perspectives, and challenges
Shijie Shi, Kaidi Zhang, Na Tian, et al.
Food Research International (2025), pp. 116459-116459
Closed Access
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