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Paracoccidioidomycosis (PCM) is a systemic mycosis caused by fungi of the genus Paracoccidioides. Serological tests are auxiliary in the diagnosis of PCM. However, the lack of standardization is a central problem in serodiagnosis and antibody titration. The objective of this study was to propose a methodology based on Fourier transform infrared spectroscopy (FTIR) for predicting antibody titers in patients with PCM. A total of 118 serum samples from patients with PCM were included, for which antibody titration using double immunodiffusion (DID) was previously performed. Serum samples were analyzed by attenuated total reflection (ATR)-FTIR and a supervised analysis with partial least squares regression (PLS) was used to predict the antibody titers. The PLS model with two latent variables and with the use of one orthogonal signal correction (OSC) showed a determination coefficient (R2) higher than 0.9999 for both the calibration and prediction set. The model was able to predict the antibody titers from patients with PCM with a minimal error. Therefore, modeling with FTIR/ATR and multivariate calibration proved to be a fast and highly accurate method for antibody titration, replacing the need for antigen production and performance of traditional serological tests.

Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551–1095 cm−1. The wavenumber that had the highest AUC value was 1264 cm−1, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551–1095 cm−1. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.