Wine age prediction using digital images and multivariate calibration

The prediction or confirmation of age is an important field in evaluation of wine's value. Such type of studies commonly requires a number of data sets describing changes in chemical composition and/or related physical properties. Digital images of wines captured by a webcam represent an easy and low-cost approach to prevent frauds connected to the age of wines. In this work, a combination of frequency histograms including grey scale, red-green-blue (RGB) and hue-saturation-value (HSV) colour models extracted from digital images were used to evaluate the age of botrytized and related varietal wines produced during a 1989-2019 period at different countries. The main findings showed that digital images carry the appropriate chemical information for the age assignment and PLS-type models were able to estimate wine age only one latent variable. Grey levels enabled to find figure of merit values similar to the PLS model based on full histogram. An additional interval selection of the histograms with interval PLS allows improving accuracy of variable assessment and achieving lower error at a cross-validation step, RMSECV decreases from 3.6 years to 3.1 years. When models were employed to predict an external set of samples similar results were found, RMSEP equal to 2.8 years and 2.9 years for PLS and iPLS, respectively. However, a slight deterioration of the results was observed for the PLS model based on full grey levels (RMSEP 3.2). In general, these non-destructive measurements do not generate residuals and can be performed without sophisticated equipment with a reasonably accurate response.