In this study we describe a semiautomatic Fourier transform infrared spectroscopic methodology for the analysis of liquid serum samples, which combines simple sample introduction with high sample throughput. The applicability of this new infrared technology to the analysis of liquid serum samples from a cohort of cattle naturally infected with bovine spongiform encephalopathy and from controls was explored in comparison to the conventional approach based on transmission infrared spectroscopy of dried serum films. Artifical neural network analysis of the infrared data was performed to differentiate between bovine spongiform encephalopathy-negative controls and animals in the late stage of the disease. After training of artifical neural network classifiers, infrared spectra of sera from an independent external validation data set were analyzed. In this way, sensitivities between 90 and 96% and specificities between 84 and 92% were achieved, respectively, depending upon the strategy of data collection and data analysis. Based on these results, the advantages and limitations of the liquid sample technique and the dried film approach for routine analysis of biofluids are discussed.