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Abstract The high-cost pressure caused by the level of competition poses major challenges for breweries. While microbreweries can develop local strengths and brewery groups develop synergies, this does not represent a decisive improvement. The application of machine learning, on the other hand, could give breweries a significant advantage in their brewing process. Several approaches to the application of machine learning in the brewing process have already been proposed in the literature. To guide possible areas of applications and the respective available solution approaches to improve the brewing process based on machine learning, a systematic review of the application of machine learning in the brewing process is presented in this paper. In this systematic review, all potentially relevant publications were included at first. Subsequently, irrelative publications were filtered out by using a clustering approach. Afterward, the remaining 21 publications were analyzed and synthesized. Based on a developed framework considering the brewing process steps, areas of improvement, machine learning tasks, and machine learning algorithms, these publications were classified. Upon the classification, a descriptive analysis was performed to identify common approaches in the existing literature. One result was that research on artificial intelligence in brewing lags significantly behind the general trend of artificial intelligence research. Additionally, there is very limited research into the association between the recipe and the desired chemical properties of the beer. Furthermore, it was noticeable that machine learning tasks utilizing artificial neural networks or support vector machines were preferred over others. |
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