The study determined optimal parameters to four preprocessing techniques for mid-infrared (MIR) spectra of wines and grape berry homogenates and tested MIR's ability to model sensory properties of research Cabernet Sauvignon and Chardonnay wines. Savitsky-Golay (SG) derivative, smoothing points, and polynomial order, and extended multiplicative signal correction (EMSC) polynomial were investigated as preprocessing techniques at 2, 2, 5, and 3 levels, respectively, all in combination. Preprocessed data were analysed with partial least squares regression (PLS) to model the wine sensory data and the regression coefficients of PLS calibration models (R2) were further analysed with multivariate analysis of variance (MANOVA). SG transformations were significant factors from the MANOVA that influenced R2, while EMSC did not. Overall, PLSR models that predicted wine sensory characteristics gave a poor to moderate R2. Consistently predicting wine sensory attributes within a variety and across vintages is challenging, regardless of using grape or wine spectra as predictors.