Nowadays, coffee has a broad highlight in the international commerce and its qualities are certified according to the Global Quality Program developed by specialized coffee agencies. Expert tasters provide a score classifying the coffees as Gourmet, Superior, Traditional and Inferior. However, due to relatively high-cost, possibility of misclassification, difficulty of large-scale analysis, and mainly to the subjectivity generated by the tasters, the international commerce requires a fast and reliable analysis. Fourier Transform Infrared Spectroscopy (FTIR) is a widely disseminated technique in laboratories routine for direct analysis besides successfully applied to measure the coffee quality. In the present work, coffee FTIR spectra were assisted by chemometric tools based on pattern recognition such as principal component analysis (PCA) and linear discriminant analysis (LDA) to find a suitable classification model for authentication purposes. Spectral predictors did not favor the discrimination of coffee samples. LDA performance was greatly improved using the PCs obtained from PCA as input variables. Several models were fitted from these PCs, so that the optimal classifier achieved 95 % accuracy in the overall predictions. This model also showed sensitivities in the range 83–100 % and specificities ranged from 93 to 100 %, indicating that the tuned model is trustworthy to successfully evaluate the commercial coffee authenticity. The developed method is simple, rapid, and environmentally friendly, since not demanding any sample preparation and does not producing waste after analysis. Its application can help to protect coffee market and companies by product quality certification.