Random forest and long short-term memory based machine learning models for classification of ion mobility spectrometry spectra

Patrick C. Riley; Samir V. Deshpande; Brian S. Ince; Brian C. Hauck; Kyle P. O'Donnell; Ruth Dereje; Charles S. Harden; Vincent M. McHugh; Mary M. Wade

doi:10.1117/12.2585829

12 April 2021 Random forest and long short-term memory based machine learning models for classification of ion mobility spectrometry spectra

Patrick C. Riley, Samir V. Deshpande, Brian S. Ince, Brian C. Hauck, Kyle P. O'Donnell, Ruth Dereje, Charles S. Harden, Vincent M. McHugh, Mary M. Wade

Author Affiliations +

Proceedings Volume 11749, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXII; 117490U (2021) https://doi.org/10.1117/12.2585829
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

The development of alarm algorithms in ion mobility spectrometry (IMS) based chemical vapor detection is challenged by the presence of overlapping chemical peaks. IMS technology identifies a chemical through hard-coded alarm windows. Alarm windows are designed as range of reduced mobility values, and act as an IF-THEN statement. Where if a peak forms in the region it then assigns a preset alarm label. A majority of IMS alarm algorithm design has relied on setting boundary conditions based on a statistical variance in product ion peak positions. To develop these alarm windows for IMS detectors the variance in peak position had to be captured through extensive laboratory testing. These windows are determined through time consuming and rigorous laboratory testing across multiple detectors under multiple conditions. Machine learning (ML) is a field of science that intersects with computer science and mathematics to “teach” a computer using large amounts of data. The development of traditional alarm algorithms IMS has left a plethora of data available to be explored by ML techniques. Presented here is a random forest (RF) classification model along with a long short-term memory (LSTM) based neural network model to label the spectra of IMS data with high accuracy.

Conference Presentation

Citation Download Citation

Patrick C. Riley, Samir V. Deshpande, Brian S. Ince, Brian C. Hauck, Kyle P. O'Donnell, Ruth Dereje, Charles S. Harden, Vincent M. McHugh, and Mary M. Wade "Random forest and long short-term memory based machine learning models for classification of ion mobility spectrometry spectra", Proc. SPIE 11749, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXII, 117490U (12 April 2021); https://doi.org/10.1117/12.2585829

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available