Ut-battelle, Inc.

University of Tennessee Research Foundation

Oak Ridge Associated Universities

Eric S Muckley

Ilia N Ivanov

Reinaldo C Reina

Nicholas West

A system for determining an age and/or quality of food or beverage based on one or more combinations of outputs from gas sensors input into a deployed machine learning model is provided. The system may comprise an electronic nose which may comprise a housing and the gas sensors. The housing may have an air channel. Each sensor has its active sensor portion in the air channel. A system for predicting one or more natural language descriptors associated with aromas of an item based on one or more outputs of the gas sensors and calculated one or more ratios input into a logistic regression model is also provided.

Aroma detection systems for food and beverage and conversion of detected aromas to natural language descriptors

A system for predicting one or more analytes based on outputs from thin film gas sensors is provided. The system may comprise an electronic nose (e-nose). The e-nose may comprise the gas sensors and a first processor. The system may further comprise a second processor. The second processor may be configured to receive the output from each of the gas sensors, evaluate a prediction accuracy using an evaluation parameter of each of a plurality of models which are trained and tested and select a model from among the plurality of models to deploy based on a comparison of the evaluation parameter for each of the plurality of models and use the same. The second processor may also receive, an output of each of the gas sensors caused by unknown one or more analytes; and predict, using the deployed model, the one or more analytes that causes the output.

Chemical detection system with at least one electronic nose

An additive manufacturing system comprising at least one electronic nose (e-nose) is provided. The e-nose may comprise a housing and gas sensors. The housing may have an air channel. The active sensor portion of the sensors are positioned in the air channel. The housing may be mounted to an extruder head of an additive manufacturing device. The system may also comprise a processor. The processor may determine whether there is an abnormality in an additive manufacturing process based on one or more combinations of outputs from the gas sensors received during the additive manufacturing process input into a deployed machine learning model; and generate a report for the additive manufacturing process containing the determination.

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San Diego, CA, United States of America

Eric S Muckley