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

Hua Li,Wenxiang Xiao,Zhencheng Chen,Hongda Zeng,Xiaoxia Du,Jiakai Pan

doi:10.3390/s21186160

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.

Highlights

High-field asymmetric ion mobility spectrometry (FAIMS) is a new technology that uses nonlinear ion mobility variation under high electric fields to separate and recognize materials
The same experiment was performed again by classifying the mixtures as above but first with ethyl acetate and acetone taking the place of the ethanol
This study used a deep-learning model to detect specific substances in complex mixtures via a homemade FAIMS system. This is the first application of deep learning to the analysis of FAIMS mixtures for component identification

Summary

Introduction

High-field asymmetric ion mobility spectrometry (FAIMS) is a new technology that uses nonlinear ion mobility variation under high electric fields to separate and recognize materials. FAIMS offers high sensitivity, fast detection speed, and miniaturization. It is expected to become an alternative to mass spectrometry (MS), ion mobility spectroscopy (IMS), and other analytical techniques [1,2,3,4]. FAIMS distinguishes ions using the differences between their ion mobility coefficients in low and high electric fields. When a sample is detected, FAIMS generates a unique chromatogram of the substance, which is called a fingerprint spectrum. Fingerprint spectra represent the compression of multiple FAIMS curves into a three-dimensional image, where the horizontal dimension represents the compensating voltage (CV), and the vertical dimension represents the radio frequency (RF) voltage. The intensity of the detected charged ions is presented using color

Objectives

Results

Conclusion