ArticleScientific reports2022
In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 46 citations in OpenAlex.
- Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications.The veterinary quarterly · 2026Review
- Longitudinal Analysis of Physiological Adaptation in Primiparous Dairy Cows During the Transition Period.Veterinary sciences · 2026Article
- Multivariate hematological and biochemical profiling reveals conserved physiological modules in Brahman- and Charolais-crossbred beef cattle under tropical smallholder systems.Veterinary world · 2026Article
- - Invited Review - Application of precision livestock farming: challenges and opportunities.Animal bioscience · 2026Article
- Prediction of heat stress response in dairy cows using milk mid-infrared spectra.Scientific reports · 2026Article
- The intersection of biology and advanced technologies defines the future of dairy reproductive management.JDS communications · 2025Review
- Metabolic and Physiological Predictors of Enteric Methane Emissions in Early Lactation Dairy Cows: A Prospective Observational Study.Life (Basel, Switzerland) · 2025Article
- Predicting dyscalcemia status in early-lactation multiparous Holstein cows using milk weight and constituent analysis from a single milking at 4 days in milk.Journal of dairy science · 2025Article
- Relationships Among In-Line Milk Fat-to-Protein Ratio, Metabolic Profile, and Inflammatory Biomarkers During Early Stage of Lactation in Dairy Cows.Veterinary sciences · 2025Article
- Attomolar-sensitive milk fever sensor using 3D-printed multiplex sensing structures.Nature communications · 2025Article
- From tradition to precision: leveraging digital tools to improve cattle health and welfare.Frontiers in veterinary science · 2025Review
- Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle.Journal of animal science and biotechnology · 2024Article
- Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models.Genetics, selection, evolution : GSE · 2024Article
- Integrating on-farm and genomic information improves the predictive ability of milk infrared prediction of blood indicators of metabolic disorders in dairy cows.Genetics, selection, evolution : GSE · 2023Article
- Novel prediction models for hyperketonemia using bovine milk Fourier-transform infrared spectroscopy.Preventive veterinary medicine · 2023Article
- Biological Health Markers Associated with Oxidative Stress in Dairy Cows during Lactation Period.Metabolites · 2023Review
- Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants.BMC veterinary research · 2022Article
- Relationships between Milk and Blood Biochemical Parameters and Metabolic Status in Dairy Cows during Lactation.Metabolites · 2022Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.