Raman spectroscopy for detection of clinical and subclinical mastitis
Mastitis is the both the most common and most expensive disease in the dairy industry. When it occurs in dairy cows, mastitis leads to deterioration in milk production and quality that causes a decrease in profitability. Therefore, early detection of bacterial infection in the udders is imperative to producers. While clinical mastitis causes changes in the milk that are visible to the eye, subclinical mastitis is difficult to diagnose and therefore is more prevalent and has a greater economic impact due to long-term effects on yield. For this reason, there is substantial interest in developing a diagnostic tool that can detect subclinical mastitis as early as possible.
There are several currently used tools for detecting subclinical mastitis. While direct examination of udders and milk culturing are traditional and effective methods, they are time consuming and not feasible at scale. Therefore, cellular and molecular diagnostic techniques using milk sampling such as cow-side antigen tests to detect pathogens and somatic cell counts, which are positively correlated with mastitis severity, are employed to improve the likelihood of detecting mastitis. However, these technologies are still difficult to scale for large herds, and the dairy industry would benefit from a high-throughput molecular tool that could diagnose subclinical mastitis with the accuracy of currently employed, low-throughput tests.
Researchers at The Ohio State University led by Dr. Benjamin Enger have developed a method for using Raman spectroscopy to identify subclinical mastitis by detecting the biochemical changes in milk that accompany infection. When a cow experiences infection, the composition of its milk undergoes substantial change. Therefore, this method exploits mastitis-associated change in composition to discriminate milk from healthy cows from milk that displays molecular signatures indicating the presence of infection. Using Raman spectroscopy in this way presents a new, high-throughput approach to detecting subclinical mastitis that could provide high-resolution information for herds of any size and maximize their quality of product.