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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:

Potential of deep learning and snapshot hyperspectral imaging for classification of species in meat
Mahmoud Al-Sarayreh, Marlon M. Reis, Wei Qi Yan, et al.
Food Control (2020) Vol. 117, pp. 107332-107332
Closed Access | Times Cited: 102

Showing 1-25 of 102 citing articles:

Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices
Yao Liu, Hongbin Pu, Da‐Wen Sun
Trends in Food Science & Technology (2021) Vol. 113, pp. 193-204
Closed Access | Times Cited: 231
Food quality 4.0: From traditional approaches to digitalized automated analysis
Abdo Hassoun, Sandeep Jagtap, Guillermo García-García, et al.
Journal of Food Engineering (2022) Vol. 337, pp. 111216-111216
Open Access | Times Cited: 111
Applications of machine learning techniques for enhancing nondestructive food quality and safety detection
Yuandong Lin, Ji Ma, Qijun Wang, et al.
Critical Reviews in Food Science and Nutrition (2022) Vol. 63, Iss. 12, pp. 1649-1669
Closed Access | Times Cited: 95
Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches
Mourad Kharbach, Mohammed Alaoui Mansouri, Mohammed Taabouz, et al.
Foods (2023) Vol. 12, Iss. 14, pp. 2753-2753
Open Access | Times Cited: 60
Computer vision and deep learning-based approaches for detection of food nutrients/nutrition: New insights and advances
Sushant Kaushal, Dushyanth Kumar Tammineni, Priya Rana, et al.
Trends in Food Science & Technology (2024) Vol. 146, pp. 104408-104408
Closed Access | Times Cited: 20
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: 16
Fraud in Animal Origin Food Products: Advances in Emerging Spectroscopic Detection Methods over the Past Five Years
Abdo Hassoun, Ingrid Måge, Walter Schmidt, et al.
Foods (2020) Vol. 9, Iss. 8, pp. 1069-1069
Open Access | Times Cited: 121
A novel method based on machine vision system and deep learning to detect fraud in turmeric powder
Ahmad Jahanbakhshi, Yousef Abbaspour‐Gilandeh, Kobra Heidarbeigi, et al.
Computers in Biology and Medicine (2021) Vol. 136, pp. 104728-104728
Closed Access | Times Cited: 71
Food traceability 4.0 as part of the fourth industrial revolution: key enabling technologies
Abdo Hassoun, Nour Alhaj Abdullah, Abderrahmane Aït‐Kaddour, et al.
Critical Reviews in Food Science and Nutrition (2022) Vol. 64, Iss. 3, pp. 873-889
Closed Access | Times Cited: 57
Hyperspectral Imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends
Wenyang Jia, Saskia M. van Ruth, Nigel Scollan, et al.
Current Research in Food Science (2022) Vol. 5, pp. 1017-1027
Open Access | Times Cited: 46
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: 38
Recent advances in shelf life prediction models for monitoring food quality
Fangchao Cui, Shiwei Zheng, Dangfeng Wang, et al.
Comprehensive Reviews in Food Science and Food Safety (2023) Vol. 22, Iss. 2, pp. 1257-1284
Closed Access | Times Cited: 35
Distinguishing fresh and frozen-thawed beef using hyperspectral imaging technology combined with convolutional neural networks
Hongbin Pu, Jingxiao Yu, Da‐Wen Sun, et al.
Microchemical Journal (2023) Vol. 189, pp. 108559-108559
Closed Access | Times Cited: 26
Prediction of fat content in salmon fillets based on hyperspectral imaging and residual attention convolution neural network
Wei Luo, Jing Zhang, Hai‐Hua Huang, et al.
LWT (2023) Vol. 184, pp. 115018-115018
Open Access | Times Cited: 25
Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion
Yating Hu, Benxue Ma, Huting Wang, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 24
Principles and applications of convolutional neural network for spectral analysis in food quality evaluation: A review
Na Luo, Daming Xu, Bin Xing, et al.
Journal of Food Composition and Analysis (2024) Vol. 128, pp. 105996-105996
Closed Access | Times Cited: 14
Exploring the potential of hyperspectral imaging for microbial assessment of meat: A review
Rumbidzai T. Matenda, Diane Rip, J. Marais, et al.
Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (2024) Vol. 315, pp. 124261-124261
Open Access | Times Cited: 10
Rapid analysis technologies with chemometrics for food authenticity field: A review
Zixuan Zhang, Yalan Li, Shanshan Zhao, et al.
Current Research in Food Science (2024) Vol. 8, pp. 100676-100676
Open Access | Times Cited: 8
Rapid detection of the reducing sugar and amino acid nitrogen contents of Daqu based on hyperspectral imaging
Haoping Huang, Xinjun Hu, Jianping Tian, et al.
Journal of Food Composition and Analysis (2021) Vol. 101, pp. 103970-103970
Closed Access | Times Cited: 53
Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging
Toby Collins, Marianne Maktabi, Manuel Barberio, et al.
Diagnostics (2021) Vol. 11, Iss. 10, pp. 1810-1810
Open Access | Times Cited: 51
Grading Methods for Fruit Freshness Based on Deep Learning
Yuhang Fu, Minh Nguyen, Wei Qi Yan
SN Computer Science (2022) Vol. 3, Iss. 4
Open Access | Times Cited: 33
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: 30
A Hyperspectral Data 3D Convolutional Neural Network Classification Model for Diagnosis of Gray Mold Disease in Strawberry Leaves
Dae-Hyun Jung, Jeong‐Do Kim, Ho‐Youn Kim, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 29
Deep learning-based multi-spectral identification of grey mould
Nikolaos Giakoumoglou, Eleftheria Maria Pechlivani, Athanasios Sakelliou, et al.
Smart Agricultural Technology (2023) Vol. 4, pp. 100174-100174
Open Access | Times Cited: 20
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: 20
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