Improving Bovine Brucellosis Diagnostics: Rapid, Accurate Detection via Blood Serum Infrared Spectroscopy and Machine Learning

Diagnosing bovine brucellosis is a major challenge due to its significant economic impact, causing losses in meat and dairy production and its potential to transmit to humans. In Brazil, disease control relies on diagnosis, animal culling, and vaccination. However, existing diagnostic tests, despite their quality, are time-consuming and prone to false positives and negatives, complicating effective control. There is a critical need for a low-cost, fast, and accurate diagnostic test for large-scale use. Spectroscopy techniques combined with machine learning show great promise for improving diagnostic tests. Here, we explore the potential use of FTIR (Fourier transform infrared) spectroscopy and machine learning algorithms to provide a rapid, accurate, and cost-effective diagnostic method for Brucella abortus. This study explored the use of FTIR spectroscopy on bovine blood serum in liquid and dried forms to develop a new photodiagnosis method. Eighty bovine blood serum samples (40 infected and 40 control animals) were analyzed. Initially, the FTIR data were pretreated using the standard normal deviate method to remove baseline deviations. Principal component analysis was then applied to observe clustering tendencies, and the further selection of principal components improved clustering. Using support vector machine algorithms, the predictive models achieved overall accuracies of 95.8% for dried samples and 91.7% for liquid samples. This new methodology delivers results in about 5 min, compared to the 48 h required for standard diagnostic methods. These findings demonstrate the viability of this approach for diagnosing bovine brucellosis, potentially enhancing disease control programs in Brazil and beyond.