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前LEC:通过改善弱峰感知来增强FTIR频谱中的功能组识别

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

概括

一种名为LEC-former的新模型通过更好地分析弱光谱峰值,改善了来自红外光谱的功能组识别. 这种先进的技术提高了分子分析的准确性,用于识别未知的化合物.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
计算化学计算化学

背景情况:

福里埃变换红外光谱 (FTIR) 对于化学结构分析和识别未知的化合物至关重要.
传统方法难以利用弱光谱特征,限制功能组识别的准确性.
机器学习模型经常忽略微妙的峰值,阻碍了全面的光谱分析.

研究的目的:

在FTIR频谱中引入LEC-former,这是一种用于增强功能组识别的新型模型.
改善在红外分析中弱光谱峰值的表示和利用.
用光谱数据提高识别功能组和匹配分子的准确性.

主要方法:

开发了LEC-former,结合了长距离光谱峰值依赖的自我注意机制.
集成了一个LEC模块,用于增强局部光谱特征的细粒度感知.
利用来自NIST化学网络书的23337个FTIR光谱的数据集进行多标签功能组识别.

主要成果:

与主流模型相比,LEC-former在功能组识别准确度方面表现出显著的改善.
该模型显示了弱光谱峰值的增强表示和峰值位置的整合.
在分子准确匹配率方面取得了出色的表现,验证了其有效性.

结论:

通过有效利用弱频谱信号,LEC-former提供了一种优越的功能组识别方法.
该模型能够捕捉远程和局部光谱依赖性,从而提高分析能力.
这一进步为在化学分析中准确识别未知的化合物具有重大潜力.

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