Enders Abigail A, North Nicole M, Fensore Chase M, Velez-Alvarez Juan, Allen Heather C
Department of Chemistry & Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States.
Anal Chem. 2021 Jul 20;93(28):9711-9718. doi: 10.1021/acs.analchem.1c00867. Epub 2021 Jun 30.
Fourier transform infrared spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using convolutional neural networks (CNNs) to identify the presence of functional groups in gas-phase FTIR spectra. The ML models reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas-phase organic molecules within the NIST spectral database and transform the data into spectral images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that infer in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method.
傅里叶变换红外光谱法（FTIR）是一种广泛应用的光谱技术。光谱解析是一个耗时的过程，但它能提供有关化合物和复杂物质中存在的官能团的重要信息。我们通过使用卷积神经网络（CNN）的机器学习（ML）算法开发了一个可推广的模型，以识别气相FTIR光谱中官能团的存在。ML模型减少了分析官能团所需的时间，并有助于FTIR光谱的解析。通过网络爬虫，我们从美国国家标准与技术研究院（NIST）光谱数据库中的8728个气相有机分子获取强度-频率数据，并将数据转换为光谱图像。我们成功地训练了15种最常见有机官能团的模型，然后通过对先前未训练的光谱进行识别来确定这些官能团。这些模型有助于扩展FTIR测量在有机样品简便分析中的应用。我们的方法是构建广泛的官能团模型，这些模型串联推断以提供光谱的完整解析。我们展示了首次使用基于图像的CNN进行ML以从光谱方法预测官能团的实现。
