Neural network classification of mobility spectra for volatile organic compounds using tandem differential mobility spectrometry with field induced fragmentation
Neural network classification of mobility spectra for volatile organic compounds using tandem differential mobility spectrometry with field induced fragmentation
复制标题
使用串联微分迁移谱法和场诱导碎裂对挥发性有机化合物的迁移谱进行神经网络分类
DOI：
10.1016/j.aca.2023.341047
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发表时间：
2023
影响因子：
6.2
通讯作者：
Eiceman, G.A.
中科院分区：
文献类型：
--
作者：
Fowler, P.E.;Bernat, T.;Pilgrim, J.Z.;Eiceman, G.A.
使用串联微分迁移谱 (DMS) 获得了来自 5 个化学类别的 45 种含氧挥发性有机化合物的场诱导碎裂 (FIF) 光谱库。质子化单体在第一个 DMS 阶段进行迁移率分离，在中间（或反应）阶段用 >10,000 V/cm 的电场进行碎片化，并在第二个 DMS 阶段表征迁移率。其他光谱库是通过单个 DMS 阶段获得的质子化单体和完整的迁移率光谱。来自内部准备的 Python/Tensorflow 软件和商业 NeuralWorks Professional II/PLUS 的神经网络经过训练，可以将光谱分配到化学类别中。分类是否成功取决于这三个库中熟悉和不熟悉的光谱。 FIF 谱的分类测试分数最好，熟悉的化合物 >0.99，不熟悉的化合物 >0.52，与其他谱库相比，与从碎片离子中学习结构信息的神经网络一致。引入雷达图作为分类措施和探索错误分类的工具。这项工作表明，使用多级串联 DMS 进行离子碎裂预示着分子鉴定具有环境压力离子淌度分析仪的便携性和稳健性。
A spectral library of field induced fragmentation (FIF) spectra for 45 oxygen-containing volatile organic compounds from 5 chemical classes was obtained using tandem differential mobility spectrometry (DMS). Protonated monomers were mobility isolated in a first DMS stage, fragmented with electric fields >10,000 V/cm in a middle (or reactive) stage, and mobility characterized in a second DMS stage. Other spectral libraries were obtained for protonated monomers and for complete mobility spectra from a single DMS stage. Neural networks from Python/Tensorflow software, prepared in-house, and from commercial NeuralWorks Professional II/PLUS were trained to assign spectra into a chemical class. The success at classification was determined for familiar and unfamiliar spectra from these three libraries. Classification test scores were best with FIF spectra with >0.99 for familiar compounds and 0.52 for unfamiliar compounds and were consistent with neural network learning of structural information from fragment ions when compared to other spectral libraries. Radar charts are introduced as measures of classification and as a tool to explore mis-classification. This work shows that ion fragmentation with multi-stage tandem DMS portends molecular identification with the portability and robustness of ambient pressure ion mobility analyzers.