BEVERAGE FLAVORING CONTROL SYSTEM

Abstract: a method of training a machine learning model for evaluating flavor profiles of an alcoholic beverage includes a spectral sensor collecting optical properties of the beverage or flavoring agent suspended in the beverage within a container. temperature and pressure sensors collect environment data of the container during a flavoring session. a control apparatus displays a user interface requesting a taste tester to select labels for the session. a control apparatus memory stores selected labels responsive to the taste tester's selections. a feature extraction module extracts features from the optical properties and environment data based on predetermined criteria. a machine learning engine trains a machine learning model to continuously learn flavor characteristics under a plurality of flavoring sessions using the extracted features and selected labels. the trained machine learning model is deployed for quality control and for determining the difference between a good batch and poor batch of the alcoholic beverage.

Inventor(s): Christopher Singleton

CPC Classification: G01N33/146 (INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing processes other than immunoassay, involving enzymes or microorganisms , ))

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