Using machine learning, robots to assess beer quality

Sensory attributes of beer are directly linked to perceived foam-related parameters and beer color. In a study published in the Journal of Food Science, researchers developed an objective predictive model using machine learning modeling to assess the intensity levels of sensory descriptors in beer using the physical measurements of color and foam‐related parameters.

The researchers used a robotic pourer (RoboBEER) to obtain 15 color and foam‐related parameters from 22 different commercial beer samples. They then conducted a sensory session using quantitative descriptive analysis with trained panelists to assess the intensity of 10 beer descriptors.

The results showed that the principal component analysis explained 64% of data variability with correlations found between foam‐related descriptors from sensory and RoboBEER, such as the positive and significant correlation between carbon dioxide and carbonation mouthfeel, correlation of viscosity to sensory, and maximum volume of foam and total lifetime of foam.

Using the RoboBEER parameters as inputs, an artificial neural network (ANN) regression model showed high correlation to predict the intensity levels of 10 related sensory descriptors such as yeast, grains and hops aromas, hops flavor, bitter, sour and sweet tastes, viscosity, carbonation, and astringency.

The researchers concluded that “the use of RoboBEER to assess beer quality showed to be a reliable, objective, accurate, and less time‐consuming method to predict sensory descriptors compared to trained sensory panels. Hence, this method could be useful as a rapid screening procedure to evaluate beer quality at the end of the production line for industry applications.”