Rosé or white, glass or plastic: computer vision and machine learning study of cavitation bubbles in sparkling wines

Abstract

This study presents a machine learning (ML)/Artificial Intelligence (AI) approach to classify types of sparkling wines (champagnes) and their respective containers using image data of bubble patterns. Sparkling wines are oversaturated with dissolved CO₂, which results in extensive bubbling when the wine bottle is uncorked. The nucleation and properties of bubbles depend on the chemical composition of the wine, the properties of the glass, and the concentration of CO₂. For carbonated liquids supersaturated with CO₂, the interaction of natural and cavitation bubbles is a non-trivial matter. We study ultrasonic cavitation bubbles in two types of sparkling wines and two types of glasses with the computer vision (CV) analysis of video images and clustering using an artificial neural network (NN) approach. By integrating a segmentation NN to filter out irrelevant frames and applying the Contrastive Language-Image Pre-Training (CLIP) NN for feature embedding, followed by TabNet for classification, we demonstrate a novel application of ML/AI for distinguishing champagne characteristics. The results show that the bubbles are significantly different to be classified by the ML techniques for different types of wine and glasses. Consequently, our study demonstrates that CV/AI/ML analysis of ultrasound cavitation bubbles can be used to analyze carbonated liquids.