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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:

Comparison of Bayesian and partial least squares regression methods for mid-infrared prediction of cheese-making properties in Montbéliarde cows
Mohammed El Jabri, Marie-Pierre Sanchez, Philippe Trossat, et al.
Journal of Dairy Science (2019) Vol. 102, Iss. 8, pp. 6943-6958
Open Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods
Maria Frizzarin, Isobel Claire Gormley, D.P. Berry, et al.
Journal of Dairy Science (2021) Vol. 104, Iss. 7, pp. 7438-7447
Open Access | Times Cited: 63
Rapid determination of reducing sugar content in sweet potatoes using NIR spectra
Hong-Ju He, Yangyang Wang, Mian Zhang, et al.
Journal of Food Composition and Analysis (2022) Vol. 111, pp. 104641-104641
Closed Access | Times Cited: 42
Spectroscopic technologies and data fusion: Applications for the dairy industry
Elena Hayes, Derek Greene, Colm P. O’Donnell, et al.
Frontiers in Nutrition (2023) Vol. 9
Open Access | Times Cited: 28
Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging
Hong-Ju He, Yuling Wang, Yangyang Wang, et al.
Food Chemistry X (2023) Vol. 18, pp. 100631-100631
Open Access | Times Cited: 27
Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions
Clément Grelet, Pierre Dardenne, Hélène Soyeurt, et al.
Methods (2020) Vol. 186, pp. 97-111
Closed Access | Times Cited: 62
Real-time milk analysis integrated with stacking ensemble learning as a tool for the daily prediction of cheese-making traits in Holstein cattle
Lúcio Flávio Macêdo Mota, Diana Giannuzzi, Vittoria Bisutti, et al.
Journal of Dairy Science (2022) Vol. 105, Iss. 5, pp. 4237-4255
Open Access | Times Cited: 25
Towards achieving online prediction of starch in postharvest sweet potato [Ipomoea batatas (L.) Lam] by NIR combined with linear algorithm
Hong-Ju He, Yangyang Wang, Yuling Wang, et al.
Journal of Food Composition and Analysis (2023) Vol. 118, pp. 105220-105220
Closed Access | Times Cited: 13
The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle
Kathryn Tiplady, Thomas Lopdell, Mathew D. Littlejohn, et al.
Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 38
Application of machine-learning methods to milk mid-infrared spectra for discrimination of cow milk from pasture or total mixed ration diets
Maria Frizzarin, Tom F. O’Callaghan, Thomas Brendan Murphy, et al.
Journal of Dairy Science (2021) Vol. 104, Iss. 12, pp. 12394-12402
Open Access | Times Cited: 31
Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data
Lúcio Flávio Macêdo Mota, Sara Pegolo, Toshimi Baba, et al.
Journal of Dairy Science (2021) Vol. 104, Iss. 7, pp. 8107-8121
Open Access | Times Cited: 27
Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods
Chu Chu, Haitong Wang, Xuelu Luo, et al.
Foods (2023) Vol. 12, Iss. 20, pp. 3856-3856
Open Access | Times Cited: 11
A Review on MIR, NIR, Fluorescence and Raman Spectroscopy Combined with Chemometric Modeling to Predict the Functional Properties of Raw Bovine Milk
Áine M. Ní Fhuaráin, Colm P. O’Donnell, Jiani Luo, et al.
ACS Food Science & Technology (2024)
Open Access | Times Cited: 4
Rapid Analysis of Starch, Sugar, and Amylose in Fresh Yam Tubers and Boiled Yam Texture Using Near-Infrared Hyperspectral Imaging and Chemometrics
Michael Adesokan, Emmanuel Oladeji Alamu, Bolanle Otegbayo, et al.
Journal of Food Composition and Analysis (2025), pp. 107425-107425
Closed Access
A Review Towards Hyperspectral Imaging for Real-Time Quality Control of Food Products with an Illustrative Case Study of Milk Powder Production
Asma Khan, Muhammad Tajammal Munir, Wei Yu, et al.
Food and Bioprocess Technology (2020) Vol. 13, Iss. 5, pp. 739-752
Closed Access | Times Cited: 30
Application of infrared spectroscopic techniques to cheese authentication: A review
Larissa K R Silva, Leandro Soares Santos, Sibelli Passini Barbosa Ferrão
International Journal of Dairy Technology (2022) Vol. 75, Iss. 3, pp. 490-512
Closed Access | Times Cited: 16
Comparison of the genetic characteristics of directly measured and Fourier-transform mid-infrared-predicted bovine milk fatty acids and proteins
Kathryn Tiplady, Thomas Lopdell, Richard G. Sherlock, et al.
Journal of Dairy Science (2022) Vol. 105, Iss. 12, pp. 9763-9791
Open Access | Times Cited: 14
Prediction of Milk Coagulation Properties and Individual Cheese Yield in Sheep Using Partial Least Squares Regression
M. Cellesi, Fabio Correddu, M. G. Manca, et al.
Animals (2019) Vol. 9, Iss. 9, pp. 663-663
Open Access | Times Cited: 21
Effect of pasteurization on coagulation properties of bovine milk and the role of major composition traits and protein fractions
Marco Franzoi, Angela Costa, V. Vigolo, et al.
Journal of Food Composition and Analysis (2022) Vol. 114, pp. 104808-104808
Open Access | Times Cited: 12
An open-access computer image analysis (CIA) method to predict meat and fat content from an android smartphone-derived picture of the bovine 5th-6th rib
Bruno Meunier, Jérôme Normand, Benjamin Albouy-Kissi, et al.
Methods (2020) Vol. 186, pp. 79-89
Open Access | Times Cited: 16
Energy balance of dairy cows predicted by mid-infra-red spectra data of milk using Bayesian approaches
G. Rovere, Gustavo de los Campos, Grum Gebreyesus, et al.
Journal of Dairy Science (2023) Vol. 107, Iss. 3, pp. 1561-1576
Open Access | Times Cited: 5
Cheese yield and nutrients recovery in the curd predicted by Fourier-transform spectra from individual sheep milk samples
Michele Pazzola, Giorgia Stocco, Alessandro Ferragina, et al.
Journal of Dairy Science (2023) Vol. 106, Iss. 10, pp. 6759-6770
Open Access | Times Cited: 4
Standardization method, testing scenario, and accuracy of the infrared prediction model affect the standardization accuracy of milk mid-infrared spectra
Wenqi Lou, Haibo Lu, Xiaoli Ren, et al.
Journal of Dairy Science (2024) Vol. 107, Iss. 11, pp. 9404-9414
Open Access | Times Cited: 1
The use of milk Fourier-transform mid-infrared spectroscopy to diagnose pregnancy and determine spectral regional associations with pregnancy in US dairy cows
Piush Khanal, Robert J. Tempelman
Journal of Dairy Science (2022) Vol. 105, Iss. 4, pp. 3209-3221
Open Access | Times Cited: 7
Prediction accuracies of cheese-making traits using Fourier-transform infrared spectra in goat milk
Giorgia Stocco, Christos Dadousis, Michele Pazzola, et al.
Food Chemistry (2022) Vol. 403, pp. 134403-134403
Open Access | Times Cited: 6
Phenotypic Analysis of Fourier-Transform Infrared Milk Spectra in Dairy Goats
Bartolo de Jesús Villar‐Hernández, Nicolò Amalfitano, Alessio Cecchinato, et al.
Foods (2023) Vol. 12, Iss. 4, pp. 807-807
Open Access | Times Cited: 3
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