Identification of Specific Substances in the FAIMS Spectra of Complex Mixtures Using Deep Learning

Abstract

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.

Machine learning and signal processing assisted differential mobility spectrometry (DMS) data analysis for chemical identification

“…Machine learningassisted differentiation of DMS data has been studied for differentiating chemicals from food, 24,25 controlled chemical sources, 17 and diagnosing diseases. [12][13][14] In a pioneering work, Li et al 26 adopted deep learning to identify specic substances in the DMS spectra and reported excellent accuracy.…”