244. Applications of machine learning and graphical databases on human sensory and production data for detection, prediction, back-tracing, and mitigation of flaws, taints, and contaminations

Zachary Bushman (1), Ryan Ahn (1), Benson Chong (1), Jason Cohen (1); (1) Analytical Flavor Systems, State College, PA, U.S.A.

Sensory
Supplier Poster

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.