The impact of spectral data pre-processing on the assessment of red wine vintage through spectroscopic methods.

BACKGROUND

Red wine is a common target of fraudulent acts considering its high market value and popularity. Although there has been much effort to assess the geographical and varietal origin of wine, this is not the case for wine vintage. Vintage is a crucial parameter for the market price, especially in the case of reputable wines. Considering the season-to-season variations affecting wine quality and the ever-occurring unstable climatological conditions due to climate change, developing analytical strategies to accurately assess wine vintage is topical and of high interest.

RESULTS

In this study, we successfully employed ultraviolet-visible spectroscopy, fluorescence spectroscopy and mid-infrared spectroscopy to identify the vintage of a protected designation of origin red wine produced during four different vintages (n = 36). Class-based clustering and great discriminatory performance was achieved for the majority of the developed multivariate models and the impact of the applied spectral pre-processing was significant. Importantly, the tested scatter correction methods resulted in the best cross-validation parameters (goodness of fit, RY > 0.9 and goodness of prediction, QY > 0.8) with calculated recognition and prediction abilities in the range 77-100% and 65-96%, respectively, when using partial least squares discriminant analysis. In addition, in the case of fluorescence spectroscopy, a batch effect was revealed, which was compensated by the spectral pre-processing methods. Spectral feature selection was performed in all cases to use only the analytically important spectral signals and omit model overfitting.

CONCLUSIONS

The developed method is simple, cost-efficient and non-destructive, indicating its high potential for industrial applications as a rapid screening tool. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.