Extracting Patterns of Chemical Information from Differential Mobility Spectrometry Measurements under Varying Conditions of Humidity and Temperature

TL;DR

This study investigates how humidity and temperature affect Differential Mobility Spectrometry measurements of surgical smoke and proposes regression models to estimate measurements across varying environmental conditions, reducing the need for strict regulation.

Contribution

It introduces regression-based normalization techniques to account for environmental variability in DMS measurements, enabling more consistent analysis without strict environmental controls.

Findings

DMS measurements depend significantly on humidity and temperature.

Regression models can predict DMS measurements across different environmental conditions.

Normalization improves the comparability of DMS data from varying conditions.

Abstract

Differential Mobility Spectrometry (DMS), also known as Field Asymmetric Ion Mobility Spectrometry, is a rapid and affordable technology for extracting information from gas phase samples containing complex volatile organic compounds, and can therefore be used for analyzing surgical smoke. One obstacle to its widespread application is the dependence of DMS measurements on humidity and, to a lesser degree, temperature, making comparison of data measured under different environmental conditions arbitrary. The commonly used solution is to regulate these environmental conditions to some predefined humidity and temperature levels. However, this approach is often unfeasible or even impossible. Therefore, in this paper we analyzed a dataset of 1,852 DMS measurements of surgical smoke evaporated from porcine adipose and muscle tissue to get an understanding of the impact of varying humidity and temperature on DMS measurements. Our analysis confirmed clear dependence of the measurements on these two factors. To overcome this challenge, we fitted regression models to raw and normalized DMS measurement data. Subsequently, these models were used for estimating DMS measurements for known tissue types based on recorded humidity and temperatures. Our test suggests that it is possible to estimate DMS measurements of surgical smoke from porcine adipose and muscle tissue under specific environmental conditions by standardizing DMS measurements separation voltage-wise and training multivariate regression models on the normalized data, which is the first step in removing the need for standardized measurement conditions.