Humic acids are part of the stable organic matter fraction in soils and composts. Due to their favorable properties for soils and plants, and their role in carbon sequestration, they are considered a quality criterion of composts. Time-consuming chemical extraction of humic acids and the inherent source of errors require alternative approaches for humic acids quantification. Different measurement techniques in the mid-infrared (MIR: KBr pellet technique) and near-infrared (NIR: fiber probe as well as an integrating sphere with a sample rotator) regions were applied. Partial least squares regression (PLSR) models based on infrared spectra were developed to determine humic acids contents in composts. As the wavenumber regions used (NIR: 6105-5380 cm(-1) and 4360-4220 cm(-1), MIR: 1745-1685 cm(-1) and 1610-1567 cm(-1)) represent different molecular vibrations, the importance of the methylene-group-derived vibrations for the NIR models is discussed. The correlation coefficients obtained for the KBr pellet technique, the NIR fiber probe technique, and the NIR integrating sphere (r = 0.94, 0.93, and 0.94) and the root mean square errors of cross-validation (RMSECV = 2.2% organic dry matter (ODM), 2.5% ODM, and 2.2% ODM) make the models appropriate for application in composting practice.