Quantitative Structure-Property Relationship Study to Predict the Retention Times of Some Volatile Compounds in Rosé Wines

Abstract

In this work, quantitative structure-property relationship (QSPR) models for prediction of retention time (RT) of 47 volatile compounds in rosé wines on the basis of their molecular structures were developed by applying multiple linear regression (MLR) and artificial neural network (ANN). A Levenberg- Marquardt algorithm trained feed-forward back-propagation artificial neural network (ANN) was employed. The data were randomly divided into 29 training and 9 validation sets. For comparison purpose, multiple linear regression (MLR) model of the same data was developed. Cross-validation was used to validate the QSPR models. Also, the application of the models for prediction of external set’s retention times of compounds without any contribution to model development steps was another validation process. The MLR model yielded marginally acceptable statistics with test correlation R2 = 0.993 and mean squared error (MSE) = 0.0075. Not surprisingly, the ANN model was significantly more accurate with test correlation R2 = 0.995 and mean squared error (MSE) = 0.00013.