Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
The goal of this project is to train a Convolutional Neural Network (CNN) to recognize carbonyl groups in infrared (IR) spectra. A carbonyl group is defined by a characteristic C=O double bond, which produces a strong, easily recognizable absorption peak near 1700 cm⁻¹. To develop and evaluate the model, I am using spectra prepared through three common techniques: KBr disc, nujol mull, and liquid film. Among these, liquid-film spectra provide the cleanest signal and most closely resemble what a chemist visually relies on when identifying carbonyls. In contrast, both the KBr disc and nujol mull methods require mixing the target compound with additional materials, which can introduce interference and make automated detection more challenging.
Included in
UR-0225 Carbonyl Detection in IR Using Deep Learning
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
The goal of this project is to train a Convolutional Neural Network (CNN) to recognize carbonyl groups in infrared (IR) spectra. A carbonyl group is defined by a characteristic C=O double bond, which produces a strong, easily recognizable absorption peak near 1700 cm⁻¹. To develop and evaluate the model, I am using spectra prepared through three common techniques: KBr disc, nujol mull, and liquid film. Among these, liquid-film spectra provide the cleanest signal and most closely resemble what a chemist visually relies on when identifying carbonyls. In contrast, both the KBr disc and nujol mull methods require mixing the target compound with additional materials, which can introduce interference and make automated detection more challenging.