Work

Machine-Learning Approach to Identify Organic Functional Groups from FT-IR and NMR Spectral Data

Year: 2025

Type: article

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.

Source: ACS Omega

DOI: 10.1021/acsomega.5c01903

Authors: Gwanho Lee, Hyekyoung Shim, Juhyun Cho, Sang-Il Choi

Institution: Kyungpook National University

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,935

Funder: National Research Foundation of Korea