Merging vibrational spectroscopic data for wine classification according to the geographic origin

The wine making procedure is no longer a secret and it is nowadays well described and repeated around the world. Nevertheless, wines present unique features, strongly associated with their geographic origin. Classification systems were developed to catalogue wines according to the provenance, and are currently established by official authorities in order to ensure wine authenticity. The use of near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy for tracing the origin of wine samples, has been reported with different levels of success. This work evaluated and compared the performance of these techniques, as well as their joint use, in terms of geographic origin classification. NIR, MIR and Raman spectra of wine samples belonging to four Portuguese wine regions (Vinhos Verdes, Lisboa, Acores and Tolvora-Varosa) were analyzed by partial least squares discriminant analysis (PLS-DA). Results revealed the better suitability of MIR spectroscopy (87.7% of correct predictions) over NIR (60.4%) and Raman (60.8%). The joint use of spectral sets did not improve the predictive ability of the models. The best models were achieved by combining MIR and NIR spectra resulting in 86.7% of correct predictions. Multiblock partial least squares (MB-PLS) models were developed to further explore the combination of spectral data. Although these models did not improve the percentage of correct predictions, they demonstrated the higher contribution of MIR spectroscopic data, in the development of the models.