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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:

Identification of adulterated milk powder based on convolutional neural network and laser-induced breakdown spectroscopy
Weihua Huang, Lianbo Guo, Weiping Kou, et al.
Microchemical Journal (2022) Vol. 176, pp. 107190-107190
Closed Access | Times Cited: 50

Showing 1-25 of 50 citing articles:

Recent advances in artificial intelligence towards the sustainable future of agri-food industry
Pinku Chandra Nath, Awdhesh Kumar Mishra, Ramesh Sharma, et al.
Food Chemistry (2024) Vol. 447, pp. 138945-138945
Closed Access | Times Cited: 51
Spectroscopic food adulteration detection using machine learning: Current challenges and future prospects
Rishabh Goyal, Poonam Singha, Sushil Kumar Singh
Trends in Food Science & Technology (2024) Vol. 146, pp. 104377-104377
Closed Access | Times Cited: 46
Artificial intelligence-based techniques for adulteration and defect detections in food and agricultural industry: A review
Suhaili Othman, Nidhi Rajesh Mavani, M.A. Hussain, et al.
Journal of Agriculture and Food Research (2023) Vol. 12, pp. 100590-100590
Open Access | Times Cited: 45
Deep leaning in food safety and authenticity detection: An integrative review and future prospects
Yan Wang, Hui‐Wen Gu, Xiaoli Yin, et al.
Trends in Food Science & Technology (2024) Vol. 146, pp. 104396-104396
Closed Access | Times Cited: 31
Microstructure classification of steel samples with different heat-treatment processes based on laser-induced breakdown spectroscopy (LIBS)
Minchao Cui, Guangyuan Shi, Lingxuan Deng, et al.
Journal of Analytical Atomic Spectrometry (2024) Vol. 39, Iss. 5, pp. 1361-1374
Closed Access | Times Cited: 31
Machine learning in laser-induced breakdown spectroscopy: A review
Zhongqi Hao, Ke Liu, Qianlin Lian, et al.
Frontiers of Physics (2024) Vol. 19, Iss. 6
Closed Access | Times Cited: 25
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: 21
A critical review of recent trends in sample classification using Laser-Induced Breakdown Spectroscopy (LIBS)
Lukas Brunnbauer, Zuzana Gajarska, Hans Lohninger, et al.
TrAC Trends in Analytical Chemistry (2022) Vol. 159, pp. 116859-116859
Open Access | Times Cited: 66
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: 15
Hyperspectral imaging combined with convolutional neural network for accurately detecting adulteration in Atlantic salmon
Peng Li, Shuqi Tang, Shenghui Chen, et al.
Food Control (2022) Vol. 147, pp. 109573-109573
Closed Access | Times Cited: 38
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: 15
Detecting Whey Adulteration of Powdered Milk by Analysis of Volatile Emissions using a MOS Electronic nose
Pouya Darvishi, Esmaeil Mirzaee‐Ghaleh, Zeynab Ramedani, et al.
International Dairy Journal (2024) Vol. 157, pp. 106012-106012
Closed Access | Times Cited: 6
Application of deep learning in laser-induced breakdown spectroscopy: a review
Chu Zhang, Lei Zhou, Fei Liu, et al.
Artificial Intelligence Review (2023) Vol. 56, Iss. S2, pp. 2789-2823
Closed Access | Times Cited: 12
Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy
Jia-Xin Li, Chun-Chun Qing, Xiu-Qian Wang, et al.
Current Research in Food Science (2024) Vol. 8, pp. 100782-100782
Open Access | Times Cited: 4
Spectroscopic techniques for authentication of animal origin foods
Vandana Chaudhary, Priyanka Kajla, Aastha Dewan, et al.
Frontiers in Nutrition (2022) Vol. 9
Open Access | Times Cited: 19
Atomic spectrometry update: review of advances in the analysis of clinical and biological materials, foods and beverages
Marina Patriarca, Nicola Barlow, Alan Cross, et al.
Journal of Analytical Atomic Spectrometry (2023) Vol. 38, Iss. 3, pp. 496-577
Open Access | Times Cited: 11
Determination of watermelon soluble solids content based on visible/near infrared spectroscopy with convolutional neural network
Guantian Wang, Xiaogang Jiang, Xiong Li, et al.
Infrared Physics & Technology (2023) Vol. 133, pp. 104825-104825
Closed Access | Times Cited: 11
A Study on the Effect of Preprocessing and Normalization on Classification of Plant Samples in Machine Learning Assisted Laser-Induced Breakdown Spectroscopy
Muhammad Haider Zaman, Fahad Rehman, Muhammad Tahir, et al.
Arabian Journal for Science and Engineering (2024) Vol. 49, Iss. 7, pp. 10003-10019
Closed Access | Times Cited: 4
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 comprehensive review of direct, indirect, and AI-based detection methods for milk powder
Y. X. Song, Shen Song, Guanjun Dong, et al.
Frontiers in Sustainable Food Systems (2025) Vol. 9
Open Access
Laser Induced Breakdown Spectroscopy as an emerging technique for olive oil, milk and honey authentication and traceability: A Review
Eleni Nanou, Nefeli Pliatsika, Dimitrios Stefas, et al.
Journal of Food Composition and Analysis (2025), pp. 107650-107650
Open Access
Detection of whey protein concentrate adulteration using laser-induced breakdown spectroscopy combined with machine learning
Meiling Zhu, Weiran Song, Xuan Tang, et al.
Food Additives & Contaminants Part A (2025), pp. 1-10
Closed Access
Identification and adulteration detection of Heterotrigona itama and Apis dorsata honey using differential scanning calorimetry and convolutional neural networks with data augmentation
Norfarizah Hanim Hassan, Ngee Sing Chong, Tiem Leong Yoon, et al.
Food Chemistry (2025), pp. 144398-144398
Closed Access
Accurate Identification and Quantification of Chinese Yam Powder Adulteration Using Laser-Induced Breakdown Spectroscopy
Zhifang Zhao, Qianqian Wang, Xiangjun Xu, et al.
Foods (2022) Vol. 11, Iss. 9, pp. 1216-1216
Open Access | Times Cited: 15
Assessment of subvisible particles in biopharmaceuticals with image feature extraction and machine learning
Ravi Maharjan, Jae‐Chul Lee, Johan Bøtker, et al.
Chemometrics and Intelligent Laboratory Systems (2024) Vol. 245, pp. 105061-105061
Closed Access | Times Cited: 3
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