Machine learning and signal processing assisted differential mobility spectrometry (DMS) data analysis for chemical identification. Issue 34 (15th August 2022)

• Record Type: Journal Article
• Authors: Chakraborty, Pranay; Rajapakse, Maneeshin Y.; McCartney, Mitchell M.; Kenyon, Nicholas J.; Davis, Cristina E.
• Is Part Of: Analytical methods. Volume 14:Issue 34(2022)
• Page Start: 3315
• Page End: 3322
• Publication Date: 2022-08-15
• Subjects: Chemistry, Analytic -- Periodicals; Analytical biochemistry -- Periodicals; Chemical laboratories -- Standards -- Periodicals; 543.1905
• DOI: 10.1039/d2ay00723a
• Languages: English
• ISSNs: 1759-9660

Abstract

Abstract : The convolutional neural algorithm outperforms previously reported algorithms, and MSC approach needs minimal data for chemical identification. Abstract : Differential mobility spectrometry (DMS)-based detectors are being widely studied to detect chemical warfare agents, explosives, chemicals, drugs and analyze volatile organic compounds (VOCs). The dispersion plots from DMS devices are complex to effectively analyze through visual inspection. In the current work, we adopted machine learning to differentiate pure chemicals and identify chemicals in a mixture. In particular, we observed the convolutional neural network algorithm exhibits excellent accuracy in differentiating chemicals in their pure forms while also identifying chemicals in a mixture. In addition, we propose and validate the magnitude-squared coherence (msc) between the DMS data of known chemical composition and that of an unknown sample can be sufficient to inspect the chemical composition of the unknown sample. We have shown that the msc-based chemical identification requires the least amount of experimental data as opposed to the machine learning approach.