ArticleScientific reports2024
Enhancing early identification of high-fertile cattle females using infrared blood serum spectra and machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence and laboratory biomarkers in veterinary medicine: an update about machine learning applications.The veterinary quarterly · 2026Review
- Early and Noninvasive Bird Gender Identification by ATR-FTIR Spectra Coupled with a Randon Forest Algorithm.ACS omega · 2025Article
- Comparison of Tree-Based Machine Learning Algorithms for Classification of Livestock Breeds Based On Post-Thaw Spermatological Parameters.Veterinary medicine and science · 2025Article
- Potential use of saliva infrared spectra and machine learning for a minimally invasive screening test for congenital syphilis in infants.Scientific reports · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
Artificial insemination (AI) success in bovine reproduction is vital for the cattle industry's economic sustainability and for advancing the understanding of reproductive physiology. Identify high-fertile animals' fertility is a complex task due to multifactorial traits, including hormonal, age-related, and body condition factors. Early high-fertility identification is crucial for timely interventions and enhancing AI success. In this study, we present the potential use of Fourier-transform infrared (FTIR) spectroscopy on blood serum for early identification of high-fertile Nellore female cows for AI protocols. Blood serum FTIR spectra were obtained from Nellore female cows before AI. FTIR spectra underwent data analysis and the results demonstrated successful discrimination between animals that exhibit pregnant and non-pregnant diagnoses 30 days after AI. FTIR spectra revealed consistent vibrational modes, emphasizing Amide I and II bands. Principal Component Analysis (PCA) effectively segregated groups based on molecular information. Linear SVM with C = 10 and 4 PCs achieved 100% accuracy in the group classification. This innovative approach using FTIR spectroscopy and ML algorithms offers a promising means of high-fertile cow identification, potentially improving AI outcomes in Nellore cattle. The study presents valuable insights into advancements in reproductive management practices for this economically significant breed.
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