Guangxi Colleges and Universities Key Laboratory of Biomedical Sensing and Intelligent Instrument, Guilin University of Electronic Technology, Guilin 541004, China.
School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel). 2021 Sep 14;21(18):6160. doi: 10.3390/s21186160.
High-field asymmetric ion mobility spectrometry (FAIMS) spectra of single chemicals are easy to interpret but identifying specific chemicals within complex mixtures is difficult. This paper demonstrates that the FAIMS system can detect specific chemicals in complex mixtures. A homemade FAIMS system is used to analyze pure ethanol, ethyl acetate, acetone, 4-methyl-2-pentanone, butanone, and their mixtures in order to create datasets. An EfficientNetV2 discriminant model was constructed, and a blind test set was used to verify whether the deep-learning model is capable of the required task. The results show that the pre-trained EfficientNetV2 model completed convergence at a learning rate of 0.1 as well as 200 iterations. Specific substances in complex mixtures can be effectively identified using the trained model and the homemade FAIMS system. Accuracies of 100%, 96.7%, and 86.7% are obtained for ethanol, ethyl acetate, and acetone in the blind test set, which are much higher than conventional methods. The deep learning network provides higher accuracy than traditional FAIMS spectral analysis methods. This simplifies the FAIMS spectral analysis process and contributes to further development of FAIMS systems.
高场非对称离子迁移谱（FAIMS）的单一组分谱图易于解释，但识别复杂混合物中的特定化学物质却很困难。本文证明了 FAIMS 系统能够检测复杂混合物中的特定化学物质。使用自制的 FAIMS 系统分析纯乙醇、乙酸乙酯、丙酮、4-甲基-2-戊酮、丁酮及其混合物，以创建数据集。构建了 EfficientNetV2 判别模型，并使用盲测试集验证深度学习模型是否能够完成所需任务。结果表明，预训练的 EfficientNetV2 模型在学习率为 0.1 和 200 次迭代时完成了收敛。使用训练后的模型和自制的 FAIMS 系统可以有效地识别复杂混合物中的特定物质。在盲测试集中，乙醇、乙酸乙酯和丙酮的准确率分别达到 100%、96.7%和 86.7%，远高于传统方法。深度学习网络比传统的 FAIMS 光谱分析方法提供更高的准确性。这简化了 FAIMS 光谱分析过程，有助于 FAIMS 系统的进一步发展。
