Zachary Bushman (1), Ryan Ahn (1), Benson Chong (1), Jason Cohen (1); (1) Analytical Flavor Systems, State College, PA, U.S.A.
Latent flaws and contaminations, undetectable by most sensory and
chemical-based quality control programs, pose a risk to breweries—how
can you detect and fix the cause of a flaw that has not yet developed?
In this research, we present a novel approach to predicting latent flaws
and tracing their creation back to the root cause in the beer brewing
process. Furthermore, we show that the process may be able to predict
flaws that occur in beer after packaging and distribution, increasing
the actionability of any quality control program. At Analytical Flavor
Systems, machine learning and artificial intelligence are used to build
quality-control and flavor-profiling tools for the food and beverage
industries. By applying our algorithms to production data and human
sensory data collected with the Gastrograph review application,
predictions can be made as to the likelihood of a flaw appearing and how
to prevent, delay, or mitigate these flaws.
Zachary Bushman is a chemist at Analytical Flavor Systems and an
avid home brewer. He received his B.S. degree in chemistry from the
University of Wisconsin-Platteville in 2013. In 2015, he began working
at Analytical Flavor Systems (AFS) in State College, PA. AFS is a
company dedicated to flavor profiling and quality control in the craft
beverage industry. He is now the head chemist at AFS and directs
projects on flaw detection and hardware development.