This study represents the first attempt to combine mid infrared (MIR) spectroscopy and multivariate data processing for prediction of alcohol degree, sugars content and total acidity in straw wine. 302 Italian samples, representing different vintages, production regions and grape varieties, were analysed using FT-MIR spectroscopy and reference methods. New regression functions based on a combination of Orthogonal Signal Correction and Partial Least Squares regression are proposed for prediction of quality parameters: this approach allows overcoming the issue of matrix complexity, reducing spectral interferences and enhancing the information embodied in fingerprinting data. The models proposed are characterised by an excellent reliability, with low error in prediction (alcohol: 0.28%; sugars: 9.9 g/L; acidity: 0.29 g/L) comparable both to reference methods and table wine models. Results demonstrate that vibrational spectroscopy, combined with a proper multivariate data strategy, represents a suitable strategy for the quick and non-destructive assessment of quality parameters of straw wine.