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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:

Classification of oils and margarines by FTIR spectroscopy in tandem with machine learning
Christabel Tachie, Daniel Obiri-Ananey, Marcela Alfaro‐Córdoba, et al.
Food Chemistry (2023) Vol. 431, pp. 137077-137077
Open Access | Times Cited: 16

Showing 16 citing articles:

Hyperspectral identification of oil adulteration using machine learning techniques
Muhammad Aqeel, Ahmad Sohaib, Muhammad Naeem Iqbal, et al.
Current Research in Food Science (2024) Vol. 8, pp. 100773-100773
Open Access | Times Cited: 9
Machine Learning-Assisted FT-IR Spectroscopy for Identification of Pork Oil Adulteration in Tuna Fish Oil
Anjar Windarsih, Tri Hadi Jatmiko, Ayu Septi Anggraeni, et al.
Vibrational Spectroscopy (2024) Vol. 134, pp. 103715-103715
Closed Access | Times Cited: 5
An efficient strategy for early sex identification in Litsea cubeba based on portable Raman technology combined with machine learning algorithms
Chuyi Wang, Peipei Xu, Haonan Wang, et al.
Journal of Food Composition and Analysis (2025), pp. 107242-107242
Closed Access
Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR
A. T. Herath, Rhowell N. Tiozon, Tobias Kretzschmar, et al.
Food Chemistry (2024) Vol. 460, pp. 140728-140728
Open Access | Times Cited: 4
Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods
Jiahua Wang, Jiangjin Qian, Mengting Xu, et al.
Food Chemistry (2024) Vol. 463, pp. 141314-141314
Closed Access | Times Cited: 4
Quantification of Argan Oil (Argania spinosa L.) Adulterated with Avocado, Flaxseed, Walnut, and Pumpkin Oils Using Fourier-Transform Infrared Spectroscopy and Advanced Chemometric and Machine Learning Techniques
Linda Gjonaj, Oliver B. Generalao, Arnold C. Alguno, et al.
Chemosensors (2025) Vol. 13, Iss. 2, pp. 37-37
Open Access
Advanced machine learning techniques for hyacinth bean identification using infrared spectroscopy and computer vision
Pratik Madhukar Gorde, Poonam Singha, Sushil Kumar Singh
Sustainable Food Technology (2025)
Open Access
The Integration of Machine Learning into Proteomics Advances Food Authentication and Adulteration Control
Hongfei Li, Hanqing Mo, Yu‐Chen Song, et al.
Trends in Food Science & Technology (2025), pp. 105029-105029
Closed Access
Comparative Assessment of Kernel Chemical Properties from Six Sweet Cherry Cultivars at Four Ripening Stages
Sultan Nalçacı, Yunus Önal, Rukiye Zengin, et al.
Deleted Journal (2025) Vol. 67, Iss. 3
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
Identification and Quantification of Common Adulterants in Extra Virgin Olive Oil Using Microwave Dielectric Spectroscopy Aided by Feedforward Neural Networks
J. Alarcon, Mateus I. O. Souza, Vinícius M. Pepino, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 19, pp. 29985-29995
Closed Access | Times Cited: 1
Integrating Near-Infrared Hyperspectral Imaging with Machine Learning and Feature Selection: Detecting Adulteration of Extra-Virgin Olive Oil with Lower-Grade Olive Oils and Hazelnut Oil
Derick Malavi, Katleen Raes, Sam Van Haute
Current Research in Food Science (2024) Vol. 9, pp. 100913-100913
Open Access | Times Cited: 1
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 | Times Cited: 1
Comparison of machine learning models for classifying edible oils using Fourier‐transform infrared spectroscopy
Hyeona Lim, S. Lee, Jin Young Kim, et al.
Bulletin of the Korean Chemical Society (2024)
Closed Access | Times Cited: 1
Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market
Florina-Dorina Covaciu, Camelia Berghian‐Grosan, Ariana Raluca Hategan, et al.
Foods (2023) Vol. 12, Iss. 23, pp. 4237-4237
Open Access | Times Cited: 2
Identification and Quantification of Common Adulterants in Extra Virgin Olive Oil Using Microwave Dielectric Spectroscopy Aided by Artificial Neural Network Classifiers
J. Alarcon, Mateus I. O. Souza, Vinícius M. Pepino, et al.
(2024)
Open Access
Machine Learning Approach for High-Throughput Phenolic Antioxidant Screening in Black Rice Germplasm Collection Based on Surface Ftir
A. T. Herath, Rhowell N. Tiozon, Tobias Kretzschmar, et al.
(2024)
Closed Access
QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand
Jose Martinez-Trespalacios, Daniel E. Polo-Herrera, Tamara Y. Félix-Massa, et al.
Molecules (2024) Vol. 29, Iss. 15, pp. 3562-3562
Open Access
A comprehensive review on applications of artificial intelligence and spectroscopy for coconut oil
Iandae Tolentino Dosol, Absalom Areta Nuestro, Edwin R. Arboleda
World Journal of Advanced Research and Reviews (2023) Vol. 21, Iss. 2, pp. 518-532
Open Access | Times Cited: 1
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