Development of a rapid diagnostic method with machine learning based on serum biomarkers for the diagnosis of fatal diarrheal disease in newborn calves

This study presents an innovative diagnostic platform that enables the rapid, sensitive, and non-invasive detection of neonatal calf diarrhea through the integrated use of ATR-FTIR spectroscopy and machine learning-based predictive analysis. Neonatal calf diarrhea is one of the major diseases causing significant economic losses and animal welfare issues in cattle farming. The fact that current diagnostic methods are time-consuming and invasive limits the possibilities for early diagnosis and intervention. In this study, the unique biochemical fingerprints of serum samples obtained from healthy (S), diseased (H), and recovered (İ) calves were analyzed. The spectral data were preprocessed, reduced using Principal Component Analysis (PCA), and then classified through Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). These predictive models successfully distinguished the physiological states of the calves with high accuracy, demonstrating the reliability and potential of the proposed system as a diagnostic tool. Another novel contribution of this study is the development of a new spectrochemical indicator, the 1080 cm⁻¹/3300 cm⁻¹ index, derived from the ratio of the symmetric stretching band of PO₂⁻ to the Amide A band. This index reflects molecular changes associated with disease progression and recovery, thereby enhancing diagnostic accuracy and providing a quantitative parameter for early intervention. Additionally, by employing serum infrared spectroscopy, the study identified biomolecular markers involved in disease pathogenesis, determined critical bands within the 4000–650 cm⁻¹ wavenumber range, and analyzed qualitative and quantitative alterations in lipid, protein, and nucleic acid levels. The relationship of these changes with pathogenesis was evaluated using band area, band width, and ROC analyses. In conclusion, the transformation of spectral data into an easily interpretable metric enables the early diagnosis of neonatal calf diarrhea, contributing to improved animal welfare and the advancement of sustainable livestock management practices.