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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:

Convolutional neural networks for vibrational spectroscopic data analysis
Jacopo Acquarelli, Twan van Laarhoven, Jan Gerretzen, et al.
Analytica Chimica Acta (2016) Vol. 954, pp. 22-31
Open Access | Times Cited: 356

Showing 1-25 of 356 citing articles:

Recent advances and applications of deep learning methods in materials science
Kamal Choudhary, Brian DeCost, Chi Chen, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 626
Advancing Biosensors with Machine Learning
Feiyun Cui, Yun Yue, Yi Zhang, et al.
ACS Sensors (2020) Vol. 5, Iss. 11, pp. 3346-3364
Closed Access | Times Cited: 522
Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation
Yudong Zhang, Zhengchao Dong, Xianqing Chen, et al.
Multimedia Tools and Applications (2017) Vol. 78, Iss. 3, pp. 3613-3632
Closed Access | Times Cited: 371
DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis
Xiaolei Zhang, Tao Lin, Jinfan Xu, et al.
Analytica Chimica Acta (2019) Vol. 1058, pp. 48-57
Closed Access | Times Cited: 285
Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network
Zhengjun Qiu, Jian Chen, Yiying Zhao, et al.
Applied Sciences (2018) Vol. 8, Iss. 2, pp. 212-212
Open Access | Times Cited: 252
Deep learning for vibrational spectral analysis: Recent progress and a practical guide
Jie Yang, Jinfan Xu, Xiaolei Zhang, et al.
Analytica Chimica Acta (2019) Vol. 1081, pp. 6-17
Closed Access | Times Cited: 237
One‐dimensional convolutional neural networks for spectroscopic signal regression
Salim Malek, Farid Melgani, Yakoub Bazi
Journal of Chemometrics (2017) Vol. 32, Iss. 5
Open Access | Times Cited: 210
Recent advances of chemometric calibration methods in modern spectroscopy: Algorithms, strategy, and related issues
Haipeng Wang, Pu Chen, Jiawei Dai, et al.
TrAC Trends in Analytical Chemistry (2022) Vol. 153, pp. 116648-116648
Closed Access | Times Cited: 188
Deep learning-based component identification for the Raman spectra of mixtures
Xiaqiong Fan, Ming Wen, Huitao Zeng, et al.
The Analyst (2019) Vol. 144, Iss. 5, pp. 1789-1798
Closed Access | Times Cited: 165
Food and agro-product quality evaluation based on spectroscopy and deep learning: A review
Xiaolei Zhang, Jie Yang, Tao Lin, et al.
Trends in Food Science & Technology (2021) Vol. 112, pp. 431-441
Closed Access | Times Cited: 153
A review of artificial neural network based chemometrics applied in laser-induced breakdown spectroscopy analysis
Luning Li, Xiangfeng Liu, Fan Yang, et al.
Spectrochimica Acta Part B Atomic Spectroscopy (2021) Vol. 180, pp. 106183-106183
Closed Access | Times Cited: 124
Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee
Swathi Sirisha Nallan Chakravartula, Roberto Moscetti, Giacomo Bedini, et al.
Food Control (2022) Vol. 135, pp. 108816-108816
Closed Access | Times Cited: 119
Deep learning in analytical chemistry
B. Debus, Hadi Parastar, Peter de B. Harrington, et al.
TrAC Trends in Analytical Chemistry (2021) Vol. 145, pp. 116459-116459
Closed Access | Times Cited: 116
Deep learning for near-infrared spectral data modelling: Hypes and benefits
Puneet Mishra, Dário Passos, Federico Marini, et al.
TrAC Trends in Analytical Chemistry (2022) Vol. 157, pp. 116804-116804
Open Access | Times Cited: 105
State of the art in flexible SERS sensors toward label-free and onsite detection: from design to applications
Liping Xie, Hedele Zeng, Jiaxin Zhu, et al.
Nano Research (2022) Vol. 15, Iss. 5, pp. 4374-4394
Closed Access | Times Cited: 92
Multiclass Skin Lesion Classification Using a Novel Lightweight Deep Learning Framework for Smart Healthcare
Long Hoang, Suk‐Hwan Lee, Eung-Joo Lee, et al.
Applied Sciences (2022) Vol. 12, Iss. 5, pp. 2677-2677
Open Access | Times Cited: 79
Deep learning in spectral analysis: Modeling and imaging
Xuyang Liu, Hongle An, Wensheng Cai, et al.
TrAC Trends in Analytical Chemistry (2024) Vol. 172, pp. 117612-117612
Closed Access | Times Cited: 43
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
Artificial intelligence in metabolomics: a current review
Jinhua Chi, Jingmin Shu, Ming Li, et al.
TrAC Trends in Analytical Chemistry (2024) Vol. 178, pp. 117852-117852
Closed Access | Times Cited: 20
Deep Learning Based Module Defect Analysis for Large-Scale Photovoltaic Farms
Xiaoxia Li, Qiang Yang, Zhuo Lou, et al.
IEEE Transactions on Energy Conversion (2018) Vol. 34, Iss. 1, pp. 520-529
Closed Access | Times Cited: 161
Comprehensive study on applications of artificial neural network in food process modeling
G. V. S. Bhagya Raj, Kshirod Kumar Dash
Critical Reviews in Food Science and Nutrition (2020) Vol. 62, Iss. 10, pp. 2756-2783
Closed Access | Times Cited: 136
The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data
Wartini Ng, Budiman Minasny, Wanderson de Sousa Mendes, et al.
SOIL (2020) Vol. 6, Iss. 2, pp. 565-578
Open Access | Times Cited: 135
Machine learning applications to non-destructive defect detection in horticultural products
Jean Frederic Isingizwe Nturambirwe, Umezuruike Linus Opara
Biosystems Engineering (2019) Vol. 189, pp. 60-83
Closed Access | Times Cited: 134
Early Detection of Tomato Spotted Wilt Virus by Hyperspectral Imaging and Outlier Removal Auxiliary Classifier Generative Adversarial Nets (OR-AC-GAN)
Dongyi Wang, Robert Vinson, Maxwell Holmes, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 128
Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning
Pengcheng Nie, Jinnuo Zhang, Xuping Feng, et al.
Sensors and Actuators B Chemical (2019) Vol. 296, pp. 126630-126630
Closed Access | Times Cited: 124
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