Machine-Learning Approach to Identify Organic Functional Groups from FT-IR and NMR Spectral Data
Gwanho Lee, Hyekyoung Shim, Juhyun Cho, Sang‐Il Choi
Abstract
High Resolution Image Download MS PowerPoint Slide Interpreting spectral data to analyze the structure and properties of unknown chemicals requires a lot of time and effort. Herein, we developed a machine-learning model that simultaneously trains on multiple spectroscopic data to identify functional groups of compounds more accurately and quickly. An artificial neural network model trained on Fourier-transform infrared, proton nuclear magnetic resonance, and 13 C nuclear magnetic resonance together identified 17 functional groups with a macro-average F1 score of 0.93, outperforming the model using a single type of spectroscopy. The results indicated that training a machine-learning model with multiple spectral data can provide more accurate structural analysis when analyzing the structure of unknown chemicals, as can using multiple spectroscopy methods simultaneously.