Could QSOR Modelling and Machine Learning Techniques Be Useful to Predict Wine Aroma?

Abstract

Food informatics is having an increasing impact on the food industry and improving the quality of end products, as well as the efficiency of manufacturing processes. In the case of winemaking, a particular application of interest for food informatics is the sensory analysis of wines. This problem can benefit from the strong development that machine learning has achieved in recent decades. However, these data-driven techniques require accurate and sufficient information to generate models capable of predicting the sensory profile of wines. A review of the sensory analysis and volatile composition of wines is presented in this work, along with significant studies on the use of machine learning models to predict wine-related characteristics such as the antioxidant activity of polyphenols of wine and aroma compounds. In this sense, data from a sensory panel and analytical technology were gathered. This literature review reveals the lack of a homogeneous and sufficiently large database of sensory analysis related to the volatile composition of wines to develop machine learning models. However, among artificial intelligence approaches, the application of quantitative structure-odour relationship (QSOR) models is currently gaining importance. Recent studies show that it would be possible to predict quantitatively the sensory analysis of wines by QSOR models, using general volatile composition information. Therefore, the purpose of this review is to identify key aspects and guidelines for the development of this area.