Artificial Intelligence in Beer Brewing: A Comprehensive Review
Krzysztof Rewak 1,2,*
, Karol Zygadło 1,2
and Mateusz Jackowski 3,*
- 1 Blumilk sp. z o.o., Legnica, Poland
- 2 Faculty of Technical and Economic Sciences, Witelon Collegium State University, Legnica, Poland
- 3 Department of Micro, Nano and Bioprocess Engineering, Faculty of Chemistry, Wrocław University of Science and Technology, Wrocław, Poland
This page is a supplementary appendix to the article above, listing the publications reviewed as part of the literature review.
View source on Zenodo
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Position reflects each publication's process stage (x-axis) and methodological approach (y-axis). Click a bubble to highlight its citation links; click it again to open its source.
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This page lists all the publications related to artificial intelligence and beer brewing that we were able to find as part of our literature review.
Found a publication that's missing, incorrect, or miscategorized? Report it on GitHub .
Most publications map to a single cell, but of the sit at a category boundary. These are predominantly on the AI-approach axis: lie between computer vision and neural networks, where convolutional models serve a vision task, and between neural networks and classical methods; only are placed between adjacent process stages. Across the corpus, quality assurance ( works) and fermentation () are the most studied stages, and neural networks () is the most common method family.
Output remained low and intermittent for roughly two decades; the works published up to 2004 are dominated by neural and classical methods and are overwhelmingly concerned with fermentation. Annual counts rise from the late 2010s onward, with the highest single-year totals in and ( and publications, respectively). The works published from 2017 onward---about two thirds of the corpus---are markedly more diverse, spanning all process stages and all method families.
