As a contagious and chronic disease in the livestock industry, Paratuberculosis is a significant threat to dairy herds’ genetic and economic resources. Due to intensive breeding and high production of dairy cattle, the incidence and prevalence are higher. Developing non-destructive diagnostic methods for the early detection and identification of healthy animals is paramount for breeding programs. Conventional methods are almost entirely destructive, have low accuracy, lack precision, and are time-consuming. Near-infrared spectroscopy (NIRS) and aquaphotomics can detect changes in biofluids and thus have the potential to diagnose disease. This study aimed to investigate the diagnostic ability of NIRS and aquaphotomics for Paratuberculosis in dairy cattle.
Blood plasma from dairy cattle was collected in the NIR range (1,300 nm to 1,600 nm) 60 days before and 100 days to 200 days after calving in two groups, positive and negative, using the same consecutive enzyme-linked immunosorbent assay test results three times as a reference test.
NIRS and aquaphotomics methods invite 100% accuracy, sensitivity, and specificity to detect Paratuberculosis using data mining by unsupervised method, Principal Component Analysis, and supervised methods: Soft Independent Modeling of Class Analogiest, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Partial Least Square–Discriminant Analysis, and Support Vector Machine models.
The current study found that monitoring blood plasma with NIR spectra provides an opportunity to analyze antibody levels indirectly via changes in water spectral patterns caused by complex physiological changes, such as the amount of antibodies related to Paratuberculosis by aquagram.