Author

W. Saeys

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 4

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:chicken / avian· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Assay:metabolomics· 1

Frequent Co-Authors

B. Aernouts· 4 shared I. Adriaens· 4 shared B. De Ketelaere· 2 shared E. Stevens· 1 shared J. A. Diaz-Olivares· 1 shared J. D. Olivares· 1 shared K. Geerinckx· 1 shared M. Lidauer· 1 shared

Activity by Year

2019-2020 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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4 shared papersBioengineering

B. Aernouts

Mutual co-authors in cluster

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4 papers

Recent·Most cited

Online milk composition analysis with an on-farm near-infrared sensor

J. A. Diaz-Olivares, I. Adriaens, E. Stevens, W. Saeys, B. Aernouts

This paper presents an on-farm near-infrared sensor for real-time milk composition analysis, achieving accuracy within ICAR standards for fat and lactose but requiring calibration maintenance for protein.

bioRxiv · Jun 2, 2020Bioengineering

Mid-Infrared Spectroscopic Analysis of Raw Milk to Predict the Blood Plasma Non-Esterified Fatty Acid Concentration in Dairy Cows

B. Aernouts, I. Adriaens, J. D. Olivares, W. Saeys, P. Mäntysaari, T. Kokkonen, T. Mehtiö, S. Kajava, P. Lidauer, M. Lidauer, M. Pastell

This paper develops a model to predict blood plasma NEFA concentration in dairy cows using mid-infrared milk spectra, showing accurate predictions below 0.6 mmol/L but underestimation above 1.2 mmol/L.

bioRxiv · Nov 25, 2019Bioengineering

Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows. Timing of the alerts and robustness against missing values

I. Adriaens, O. Martin, W. Saeys, B. De Ketelaere, N. C. Friggens, B. Aernouts

This paper validates two algorithms for detecting luteolysis in dairy cows using simulated milk progesterone data, finding that PMASC detects luteolysis earlier and is more robust to missing values than a Kalman filter approach.

bioRxiv · Jun 17, 2019Bioengineering

SHORT COMMUNICATION: Validation of a novel milk progesterone based tool to monitor luteolysis in dairy cows. Performance on cost-effective, on-farm measured data

I. Adriaens, W. Saeys, K. Geerinckx, B. De Ketelaere, B. Aernouts

bioRxiv · Feb 7, 2019Bioengineering