College of Chemistry and Chemical Engineering, Central South University, Changsha, Hunan 410083, PR China.
Anal Chem. 2020 Jul 7;92(13):8649-8653. doi: 10.1021/acs.analchem.0c01450. Epub 2020 Jun 25.
Electron ionization-mass spectrometry (EI-MS) hyphenated to gas chromatography (GC) is the workhorse for analyzing volatile compounds in complex samples. The spectral matching method can only identify compounds within the spectral database. In response, we present a deep-learning-based approach (DeepEI) for structure elucidation of an unknown compound with its EI-MS spectrum. DeepEI employs deep neural networks to predict molecular fingerprints from an EI-MS spectrum and searches the molecular structure database with the predicted fingerprints. We evaluated DeepEI with MassBank spectra, and the results indicate DeepEI is an effective identification method. In addition, DeepEI can work cooperatively with database spectral matching and NEIMS (fingerprint to spectrum method) to improve identification accuracy.
电子电离-质谱（EI-MS）与气相色谱（GC）联用是分析复杂样品中挥发性化合物的主力。谱库匹配法只能鉴定谱库内的化合物。为此，我们提出了一种基于深度学习的方法（DeepEI），用于解析未知化合物的 EI-MS 谱图结构。DeepEI 采用深度神经网络从 EI-MS 谱图中预测分子指纹，并利用预测的指纹在分子结构数据库中进行搜索。我们使用 MassBank 谱图对 DeepEI 进行了评估，结果表明 DeepEI 是一种有效的鉴定方法。此外，DeepEI 可以与数据库谱库匹配和 NEIMS（指纹到谱图方法）协同工作，以提高鉴定准确性。
