Computer vision with artificial intelligence for a fast, low-cost, eco-friendly and accurate prediction of beer styles and brands

Abstract

Beer is the most consumed alcoholic beverage worldwide and are highly susceptible to fraudulent processes. Traditional sensory analysis can lack precision. With the growth of Industry 4.0, new techniques using artificial intelligence are being developed to address this issue. This scenario makes it appealing to propose low-cost techniques with broad classification capabilities based on sample fingerprints, such as computer vision (CV). CV involves image acquisition, processing, and classification using machine learning. In this work, a computer vision prototype associated with an artificial neural network was developed to classify beer in terms of style and brand. A total of 111 samples were analyzed in triplicate, with the data separated into training and testing sets. Accuracy and precision above 96% were obtained for the training set and 78% for the test set. The computer vision method proved to be a simple, low-cost, eco-friendly, and fast tool for detecting fraud in the brewing industry.