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Bovine, porcine, and fish gelatins have been differentiated based on their spectra collected by attenuated total reflectance FTIR spectroscopy (ATR-FTIRS) coupled with pattern recognition. Three tree-based classification methods, a fuzzy rule-building expert system (FuRES), support vector machine classification trees (SVMTreeG and SVMTreeH), and one reference model, super partial least-squares discriminant analysis (sPLS-DA), were evaluated with and without two preprocessing techniques, namely standard normal variate (SNV) and principal component orthogonal signal correction (PC-OSC). Validation of these methods was obtained with 95% confidence intervals with 10 bootstraps and 4 Latin partitions (10:4). The ATR-FTIR spectra were used with four different ranges: full spectra (4000-650 cm-1), fingerprint region (1731-650 cm-1), specified spectra (4000-800 cm-1), and narrow fingerprint region (1731-800 cm-1). Classification rates for the methods were improved with SNV and PC-OSC when they were used separately or together. The highest classification rates were obtained from the narrow fingerprint region with SNV and PC-OSC at 97.4 ± 1.6% for FuRES, 100 ± 0% for sPLS-DA, and 99.3 ± 0.5% for both SVMTreeG and SVMTreeH. ATR-FTIRS combined with pattern recognition is a potential analytical technique for differentiating the sources of bovine, porcine, and fish gelatins with fast and reliable results.