Fourier transform infrared spectroscopy (FT-IR) is a widely used spectroscopic method for routine analysis of substances and compounds. Spectral interpretation of spectra is a labor-intensive process that provides important information about functional groups or bonds present in compounds and complex substances. In this paper, based on deep learning methods of convolutional neural networks, models were developed to determine the presence of 17 classes of functional groups or 72 classes of coupling oscillations in the FT-IR spectra. Using web scanning, the spectra of 14 361 FT-IR spectra of organic molecules were obtained. Several different variants of model architectures with different sizes of feature maps have been tested. Based on the Shapley additive explanations (SHAP) and gradient-weighted class activation mapping (GradCAM) methods, visualization tools have been developed for visualizing and highlighting the areas of absorption bands manifestation for corresponding functional groups or bonds in the spectrum. To determine 17 and 72 classes, the F1-weighted metric, which is the harmonic mean of the class' precision and class' recall weighted by class' fraction, reached 93 and 88%, respectively, when using data on the position of absorption maxima in the spectrum as an additional source layer. The resulting model can be used to facilitate the routine analysis of spectra for all areas such as organic chemistry, materials science, and biology, as well as to facilitate the preparation of the obtained experimental data for publication.

中文翻译：

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基于多类分类卷积神经网络的傅里叶变换红外光谱识别专家系统

傅里叶变换红外光谱 (FT-IR) 是一种广泛用于物质和化合物常规分析的光谱方法。光谱的光谱解释是一个劳动密集型过程，可提供有关化合物和复杂物质中存在的官能团或键的重要信息。本文基于卷积神经网络的深度学习方法，开发了模型来确定 FT-IR 光谱中是否存在 17 类官能团或 72 类耦合振荡。利用网络扫描，获得了14 361个有机分子的FT-IR光谱。已经测试了具有不同大小特征图的模型架构的几种不同变体。基于 Shapley 加性解释 (SHAP) 和梯度加权类激活映射 (GradCAM) 方法，我们开发了可视化工具，用于可视化和突出显示光谱中相应官能团或键的吸收带表现区域。为了确定 17 和 72 个类别，当使用吸收最大值位置的数据时，F1 加权指标（按类别分数加权的类别精度和类别召回率的调和平均值）分别达到 93% 和 88%在光谱中作为附加源层。由此产生的模型可用于促进有机化学、材料科学和生物学等所有领域的光谱常规分析，并有助于准备所获得的实验数据以供发表。