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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.

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Showing 1-25 of 40 citing articles:

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges
Ramesh Sharma, Pinku Chandra Nath, Bibhab Kumar Lodh, et al.
Food Chemistry (2024) Vol. 454, pp. 139817-139817
Closed Access | Times Cited: 17
Rapid identification and quantification of vegetable oil adulteration in raw milk using a flash gas chromatography electronic nose combined with machine learning
Huaixiang Tian, Di Wu, Bin Chen, et al.
Food Control (2023) Vol. 150, pp. 109758-109758
Closed Access | Times Cited: 32
A comprehensive review of machine learning and its application to dairy products
Paulina Freire, Diego Alencar Freire, Carmen C. Licón
Critical Reviews in Food Science and Nutrition (2024), pp. 1-16
Closed Access | Times Cited: 14
Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0
Yuehua Chen, Yicheng Wang, Yiran Zhang, et al.
Foods (2024) Vol. 13, Iss. 2, pp. 235-235
Open Access | Times Cited: 9
Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study
Philip Shine, Michael D. Murphy
Sensors (2021) Vol. 22, Iss. 1, pp. 52-52
Open Access | Times Cited: 34
How Chemometrics Can Fight Milk Adulteration
Silvia Grassi, Maria Tarapoulouzi, Alessandro D’Alessandro, et al.
Foods (2022) Vol. 12, Iss. 1, pp. 139-139
Open Access | Times Cited: 27
Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms
Abdülkadir Atalan
Agribusiness (2022) Vol. 39, Iss. 1, pp. 214-241
Closed Access | Times Cited: 23
Artificial Intelligence Aided Adulteration Detection and Quantification for Red Chilli Powder
Tanmay Sarkar, Tanupriya Choudhury, Nikunj Bansal, et al.
Food Analytical Methods (2023) Vol. 16, Iss. 4, pp. 721-748
Closed Access | Times Cited: 14
Iot Based Milk Monitoring System for the Detection of Milk Adulteration
Ganesh Kumar P, A Alagammai, B S Madhumitha, et al.
ESP Journal of Engineering & Technology Advancements (2022) Vol. 2, Iss. 2, pp. 6-9
Open Access | Times Cited: 22
Rapid detection of fragrant rice adulteration by CD-IMS with machine learning
Guoxing Jing, Yuxin He, Meiyan Wang, et al.
Microchemical Journal (2025), pp. 112740-112740
Closed Access
Low-Cost, Multi-Sensor Non-Destructive Banana Ripeness Estimation Using Machine Learning
Kristian M. S. Callaghan, Uriel Martínez-Hernández
IEEE Sensors Journal (2025) Vol. 25, Iss. 5, pp. 8797-8806
Open Access
Future Trends and Prospects in Dairy Industry
Hagar F. Forsan
Sustainable development goals series (2025), pp. 383-396
Closed Access
Recent Advancements in Chemometrics based Non-Destructive Analytical Techniques for Rapid Detection of Adulterants in Milk and Dairy Products – A Review
Rui Xu, Muhammad Adil, S. Jabeen, et al.
Food Control (2025), pp. 111247-111247
Closed Access
A Novel Approach for Detection of Food Adulteration Using Deep Learning with Image Processing
Ishita Theba, Sudhir Vegad
Lecture notes in networks and systems (2025), pp. 201-218
Closed Access
Smart Milk Quality Monitoring System: IoT-Driven Sensor and Data Analytics Interface
N. Naik, Manish K Shetty, Suhas A. Bhyratae
(2025), pp. 1573-1581
Closed Access
Recent advances in detection techniques and chemometric methods for identifying adulterants in milk and dairy products
Norliza Julmohammad, Emeline Tan, Muhammad Rahimi Yusop, et al.
Food Chemistry (2025), pp. 144202-144202
Closed Access
Analysis of Protein and Fat in Milk Using Multiwavelength Gradient-Boosted Regression Tree
Tao Sheng, Shengzhe Shi, Yuanyang Zhu, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-10
Closed Access | Times Cited: 12
Swarm-Based Extreme Learning Machine Models for Global Optimization
Mustafa Abdul Salam, Ahmad Taher Azar, Rana Hussien
Computers, materials & continua/Computers, materials & continua (Print) (2021) Vol. 70, Iss. 3, pp. 6339-6363
Open Access | Times Cited: 15
Classification of Organic and Conventional Vegetables Using Machine Learning: A Case Study of Brinjal, Chili and Tomato
Sowmya Natarajan, Vijayakumar Ponnusamy
Foods (2023) Vol. 12, Iss. 6, pp. 1168-1168
Open Access | Times Cited: 6
IoT integrated fuzzy classification analysis for detecting adulterants in cow milk
Prashant P. Lal, Avishay A. Prakash, Aneesh A. Chand, et al.
Sensing and Bio-Sensing Research (2022) Vol. 36, pp. 100486-100486
Open Access | Times Cited: 8
XAI-empowered IoT multi-sensor system for real-time milk adulteration detection
Kashish Goyal, Parteek Kumar, Karun Verma
Food Control (2024) Vol. 164, pp. 110495-110495
Closed Access | Times Cited: 1
Analysis and Design of Adulteration Dairy Milk System
Mohammad Zarkasi, Diah Ayu Retnani Wulandari, Saiful Bukhori, et al.
Advances in intelligent systems research/Advances in Intelligent Systems Research (2024), pp. 190-195
Open Access | Times Cited: 1
Cost-Effective Multispectral Sensor and Artificial Neural Networks for the Detection of Starch Adulteration in Raw Milk
Yeliz Durgun, Mahmut Durgun
Applied Sciences (2024) Vol. 14, Iss. 21, pp. 9800-9800
Open Access | Times Cited: 1
Predicting adulteration of grated Parmigiano Reggiano cheese with Ricotta using electrophoresis, multivariate nonlinear regression and computational intelligence methods
Rafael Fagnani, Bruno Cesar Michelette Damião, Régia Patrícia Saviani Trentin, et al.
International Journal of Dairy Technology (2021) Vol. 75, Iss. 1, pp. 239-245
Closed Access | Times Cited: 10
Classification of Adulterated Food Grain Thermal Images Using Convolutional Neural Networks
Vijayakumar Ponnusamy, Prateek Anand, Vishvak Bhatt
Communications in computer and information science (2023), pp. 475-480
Closed Access | Times Cited: 3
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