Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

Fourier transform infrared spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using convolutional neural networks (CNNs) to identify the presence of functional groups in gas-phase FTIR spectra. The ML models reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas-phase organic molecules within the NIST spectral database and transform the data into spectral images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that infer in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method.

Automated summary

This study develops a generalizable model using machine learning algorithm with convolutional neural networks to identify functional groups in gas-phase FTIR spectra, reducing analysis time and facilitating interpretation. The model successfully trains for 15 common organic functional groups, expanding the application of FTIR measurements for easy analysis of organic samples. The approach utilizes image-based CNNs for predicting functional groups, marking the first implementation of ML in this area.