专家系统用于Fourier变换红外光谱识别基于卷积神经网络与多类分类的卷积神经网络
1Faculty of the Material Science, Lomonosov Moscow State University, Moscow, Russian Federation.
Applied spectroscopy
|January 28, 2024
概括
深度学习模型现在可以自动识别Fourier变换红外光谱 (FT-IR) 光谱中的功能组和键,显著加快化学分析. 这种人工智能工具有助于有机化学,材料科学和生物学研究人员.
科学领域:
- 频谱学是一种光谱学.
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 福里埃变换红外光谱法 (FT-IR) 对于分析化学化合物至关重要.
- 手动的频谱解释是耗时的,需要专业知识.
- 识别功能组和键是理解分子结构的关键.
研究的目的:
- 开发用于自动化FT-IR光谱分析的深度学习模型.
- 确定17个类别的功能组和72个类别的合振荡.
- 创建可视化工具来解释模型预测.
主要方法:
- 卷积神经网络 (CNN) 用于光谱分析.
- 收集了14361个有机分子FT-IR光谱的数据集.
- 沙普利增量解释 (SHAP) 和GradCAM用于可视化.
主要成果:
- 这些模型在17个类别中获得了93%的F1加权分,在72个类别中达到88%.
- 整合最大吸收位置提高了模型性能.
- 可视化工具有效地突出了相关的光谱区域.
结论:
- 深度学习为FT-IR光谱解释提供了一种有效的方法.
- 开发的模型可以加速化学,材料科学和生物学中的常规分析.
- 这种方法有助于为科学出版物准备数据.
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