LEC-former: enhancing functional group identification in FTIR spectra by improving weak peak perception.
ShiDi Xie1, LiJuan Peng1, JunNa Zhang2
1School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China. plj@swust.edu.cn.
Analytical Methods : Advancing Methods and Applications
|December 5, 2025
Summary
A new model, LEC-former, improves functional group recognition from infrared spectra by better analyzing weak spectral peaks. This advanced technique enhances molecular analysis accuracy for identifying unknown compounds.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Computational Chemistry
Background:
- Fourier transform infrared (FTIR) spectroscopy is crucial for chemical structure analysis and identifying unknown compounds.
- Traditional methods struggle to utilize weak spectral features, limiting functional group identification accuracy.
- Machine learning models often overlook subtle peaks, hindering comprehensive spectral analysis.
Purpose of the Study:
- To introduce LEC-former, a novel model for enhanced functional group recognition in FTIR spectra.
- To improve the representation and utilization of weak spectral peaks in infrared analysis.
- To advance the accuracy of identifying functional groups and matching molecules using spectral data.
Main Methods:
- Developed LEC-former, incorporating a self-attention mechanism for long-range spectral peak dependencies.
- Integrated an LEC module for enhanced fine-grained perception of local spectral features.
- Utilized a dataset of 23,337 FTIR spectra from the NIST Chemistry WebBook for multi-label functional group recognition.
Main Results:
- LEC-former demonstrated significant improvements in functional group recognition accuracy compared to mainstream models.
- The model showed enhanced representation of weak spectral peaks and integration of peak positions.
- Achieved outstanding performance in molecular exact match rate, validating its effectiveness.
Conclusions:
- LEC-former offers a superior approach to functional group recognition by effectively leveraging weak spectral signals.
- The model's ability to capture both long-range and local spectral dependencies enhances analytical capabilities.
- This advancement holds significant potential for the accurate identification of unknown compounds in chemical analysis.
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