Total monomeric anthocyanin concentration estimation of red wines based on color data using multivariate statistical analysis and machine learning.

backgroundRed wine is a high-value grape derivative containing complex chemical compounds that dictate its quality. Qualitative and quantitative determination of these compounds is essential for standardizing and classifying the product. This proof-of-concept study integrates chemometric methods and machine learning algorithms to establish predictive models for estimating total monomeric anthocyanin (TMA) concentration in red wines using non-destructive colorimetric data.

resultsImages were generated and analyzed using wine samples at three liquid depths (2, 3 and 4 mm) across three color spaces (RGB, HSV and Lab). To predict TMA content from extracted digital image features, partial least squares regression (PLSR), random forest (RF) and artificial neural network (ANN) models were evaluated using external validation and stratified five-fold cross-validation. Optimal imaging configurations were identified as 2 mm + HSV for PLSR and 2 mm + Lab for both RF and ANN. Cross-validated performance metrics yielded mean test R

conclusionUnder these controlled experimental conditions, non-linear methods (RF and ANN) demonstrated significantly stronger predictive performance than the linear PLSR model (P < 0.05). This methodology offers a rapid, non-destructive and highly promising approach for the accurate determination of TMA. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.