NIR Spectroscopy Explored as Sustainable Approach to Detecting Bovine Mastitis

In a recent study, researchers from the Universidad de Córdoba and the Centro de Investigación y Calidad Agroalimentaria del Valle de los Pedroches (CICAP) in southern Spain investigated a new method that could improve detection of subclinical bovine mastitis (SBM) in dairy cows. This study, which was published in Applied Food Research, demonstrated how near-infrared (NIR) spectroscopy can be an effective detection tool to detect SBM in cows (1). This study highlighted how NIR spectroscopy analysis of milk samples can support targeted treatment strategies while improving milk quality and animal health (1).

Led by Pablo Rodríguez-Hernández, Nieves Núñez-Sánchez, Silvia Molina-Gay, Vicente Rodríguez-Estévez, and Fernando Cardoso-Toset, the study focuses on SBM, which is a major challenge facing the global dairy industry. SBM is an infection of the mammory gland in cows (2). If left untreated, this condition can result in transient episodes of abnormal milk and can persist for the entire life of the cow (2).

Cow | Image Credit: © Dudarev Mikhail - stock.adobe.com

Subclinical mastitis poses a significant economic burden to dairy farmers. Apart from a reduced milk yield, there is an increased risk of contaminated milk entering the supply chain (1). Moreover, the continued overuse of antibiotics in livestock is a growing concern for public health, contributing to the rise of antimicrobial resistance. The researchers argue that implementing technologies like NIR spectroscopy could enable dairy producers to adopt a more precise treatment strategy that improves herd health while safeguarding food security (1).

Unlike clinical mastitis, SBM lacks visible symptoms, making timely detection difficult and contributing to unnecessary or blanket antibiotic use (1,2). In their study, the team of five researchers tested a potential fast and accurate screening method that could help detect SBM in dairy cows.

This study was conducted between March 2021 and January 2022. During this period, the team collected composite milk samples from 101 Holstein-Friesian cows housed across 29 commercial dairy herds in Córdoba, Andalusia, which is known as one of Spain’s key dairy-producing regions (1). To reduce variability in the results, the dairy cows were kept under similar housing, feeding, and milking conditions. The samples were aseptically collected from each of the four udder quarters, refrigerated, and sent to the laboratory for bacterial isolation before undergoing NIR spectroscopy analysis (1).

The researchers applied multiple discriminant models to analyze the NIR spectroscopy spectral data, aiming to classify the milk samples based on the Gram status (positive or negative) of the bacterial pathogens responsible for SBM (1).

The results suggested that the spectral fingerprints of milk samples contain enough diagnostic information to distinguish among different microbial infections reliably. The predictive models ranged in accuracy from 85.71% and 95.24% (1). Sensitivity levels managed to go up to 100% and specificity levels hovered between 81.82% and 90.91% (1).

Although their findings indicate how advanced spectroscopic tools can be integrated into routine dairy herd health management, the authors emphasized the need for further research with more diverse and larger sample sizes (1). They suggested that their method should be tested across broader populations and environmental conditions (1).

As the dairy industry continues to seek ways to balance productivity with animal welfare and environmental responsibility, innovations like NIR spectroscopy-based mastitis screening offer a compelling path forward. This study not only supports more strategic use of antibiotics, but it also aligns with global initiatives such as the One Health approach, which recognizes the interconnectedness of human, animal, and environmental health (1,3).

References

1. Rodríguez-Hernández, P.; Núñez-Sánchez, N.; Molina-Gay, S.; et al. Near Infrared Spectroscopy Analysis as a Screening Tool to Classify Milk from Bovine Subclinical Mastitis and Promote Pathogen-based Therapy. Appl. Food Res. 2025, 5 (1), 100651. DOI: 10.1016/j.afres.2024.100651
2. Wieland, M. Mastitis in Cattle. Merck Vet Manual. Available at: https://www.merckvetmanual.com/reproductive-system/mastitis-in-large-animals/mastitis-in-cattle (accessed 2025-04-21).
3. Center for Disease Control, About One Health. CDC.gov. Available at: https://www.cdc.gov/one-health/about/index.html (accessed 2025-04-21).