Infrared Spectral Analysis for Prediction of Functional Groups Based on Feature-Aggregated Deep Learning

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Spectroscopy

Background:

• Infrared (IR) spectroscopy is crucial for organic compound analysis but interpretation is complex and time-consuming.
• Manual analysis of large IR spectral datasets requires significant expertise in chemistry and spectroscopy.
• Automated methods are needed to streamline the identification of functional groups from IR spectra.

Purpose of the Study:

• To develop a novel deep learning method for automated functional group identification from IR spectra.
• To transform complex IR spectral features into intuitive, image-like feature maps for easier analysis.
• To predict major functional groups in organic molecules using convolutional neural networks.

Main Methods:

• Utilized 8272 gas-phase IR spectra from the NIST Chemistry WebBook.
• Constructed imagelike feature maps from spectral data based on intrinsic correlations.
• Developed convolutional neural network models for binary and multilabel classification of functional groups.

Main Results:

• Successfully identified 21 major functional groups per molecule using both binary and multilabel models.
• Achieved accurate predictions without requiring expert guidance or manual feature selection.
• The multilabel model provided simultaneous predictions for rapid molecular characterization.

Conclusions:

• The deep learning method effectively extracts abundant structural information from IR spectra.
• Model interpretations align with key spectral features typically considered by human spectroscopists.
• This approach shows significant potential for automated spectral identification and broader applications in data analysis.