Direct quantification of red wine phenolics using fluorescence spectroscopy with chemometrics

Abstract

Phenolic compounds are secondary metabolites known to play crucial roles in important chemical reactions impacting the mouthfeel, colour and ageing potential of red wine. Their complexity has resulted in a number of advanced analytical methods, which often prevent routine phenolic analysis in winemaking. Fluorescence spectroscopy could be an alternative to current spectrophotometric techniques and its combination with chemometrics was investigated for its suitability in directly quantifying phenolic content of unaltered red wine and fermenting samples. Front-face fluorescence was optimised and used to build predictive models for total phenols, total condensed tannins, total anthocyanins, colour density and polymeric pigments. Machine learning algorithms were used for model development. The most successful models were built for total phenols, total condensed tannins and total anthocyanins with coefficient of determination (R2cal) and RMSECV of 0.81, 0.89, 0.80 and 5.71, 104.03 mg/L, 60.67 mg/L, respectively. The validation results showed R2val values of 0.77, 0.8 and 0.77, and RMSEP values of 7.6, 172.37 mg/L and 76.57 mg/L, respectively. A novel approach for the classification of South African red wine cultivars based on unique fluorescent fingerprints was also successful with an overall cross validation score of 0.8. The best classification ability (validation score = 0.93) was shown for the data set containing only fermenting wines for the most widely represented cultivars (>20 samples). This approach may provide a useful tool for authentication and quality control by regulatory bodies.