Date of Completion

5-6-2022

Degree Type

Honors Thesis - Campus Access

Discipline

Chemistry (CHEM)

First Advisor

Dr. Emily Jarvis

Abstract

Electronic noses are devices with sensors that emulate olfaction, typically by altering the resistance of a current. Devices such as the Cyranose 320 can have built in algorithms that facilitate classification of aromas, such as Principal Component Analysis and K-Nearest Neighbors. These algorithms can be used to identify aromas present in wine samples, even without the use of specialized sampling equipment. This paper demonstrates the ability of the Cyranose 320 to identify aromas in wine samples and should serve as a basis for more sophisticated machine learning based techniques that are currently in development.

Download

Share

COinS