Spectroscopic methods have already been used as effective tools in several studies involving the detection of cancer. Fourier transform infrared spectroscopy (FTIR) has already been applied in the discrimination of cancer cells and tissues or blood of patients with the disease, observing that this technique requires the use of chemometric algorithms to obtain such results. The aim of this study was to employ a partial least squares discriminant analysis (PLS-DA) with FTIR data in the discrimination of plasma samples from patients with colorectal cancer (RCC) and healthy individuals. Multivariate analysis was performed using PLS-DA of the sample triplicates (n=90) with different types of processing. The best PLS-DA condition was obtained using the 1st derivative, 1 orthogonal signal correction (OSC) and no pre-processing. With 1 factor only, the model presented a mean square error of cross-validation (RMSECV) of 0.0004 and coefficient of determination (r^2) of 1.0000. The accuracy, precision and sensitivity of the model were 100%.