245. Beer flaw identification using a novel sensory data collection and processing technique

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

Sensory
Supplier Poster

Sensory testing is widely renowned as one of the most important steps in QC and QA in the beer industry. Next to consumer safety, the most important variable in beer production is that the flavor is pleasant and consistent. Using our novel sensory data collection tools we can collect 24 flavor variables that completely encompass gustatory flavor space (anything you can taste you can graph). This data, analyzed with our machine learning and artificial intelligence algorithms, can then be used to positively identify the 20 most common flaws with greater than 90% accuracy, even if the taster is unfamiliar with the flaw in question.

Before starting Analytical Flavor Systems, Jason was the founder and executive director of the Tea Institute at Penn State, which oversees 20+ researchers in 5 fields of study in traditional Chinese, Japanese, and Korean teas. Jason did his research in sensory science and data mining, eventually developing the Gastrograph system after three and a half years of research. Jason is a professional coffee, tea, and beer taster, and when he is not trying new products, he enjoys rock climbing, ice climbing, and fencing.