Development of Scent Detection and Categorization Algorithm Using Gas Chromatography and Machine Learning

Department

Physics and Astronomy

Authors

Alex Driehaus, Western Kentucky UniversityFollow

Document Type

Thesis

Abstract

There are many looking to connect human senses to quantifiable data. Scents are categorized by their descriptions into scent families. These include citrus, floral, and woody. Similar descriptors designate similar families, while different descriptors correlate with different families. Dravnieks compiled an Atlas of chemical descriptors [1]. Such descriptors are cinnamon, fruity, and cadaverous. By analyzing the applicability of these descriptors, the chemicals will be sorted into their scent families.

Gas chromatography generates sample-specific signals of voltage over time. Chromatograms of known scents will serve as a basis for a convolutional neural network. This algorithm will be trained on these signals and tested with unknown scents to categorize scents with no human participation. We seek to generate verbal descriptions of scent through machine learning analysis of GC signals.

Advisor(s) or Committee Chair

Ivan Novikov, Ph.D.

Disciplines

Chemistry | Computer Sciences | Physics

Recommended Citation

Driehaus, Alex, "Development of Scent Detection and Categorization Algorithm Using Gas Chromatography and Machine Learning" (2022). Mahurin Honors College Capstone Experience/Thesis Projects. Paper 972.
https://digitalcommons.wku.edu/stu_hon_theses/972