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

Philipp Müller, Gary A. Eiceman, Anton Rauhameri, Anton Kontunen, Antti Roine, Niku Oksala, Antti Vehkaoja, Maiju Lepomäki

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.

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

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.
Paper Structure (8 sections, 5 equations, 9 figures, 3 tables)

This paper contains 8 sections, 5 equations, 9 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Impact of humidity and temperature on Differential Mobility Spectrometry
  3. Data and Methods
  4. Data Collection and Description
  5. Analysis of environmental conditions
  6. Regression Methods and Analysis
  7. Results and Discussion
  8. Conclusion and outlook

Figures (9)

  • Figure 1: Example of a dispersion plot for surgical smoke from adipose tissue (top) and dispersion plot with data row-wise standardized to $[-1,1]$ from the same sample (bottom).
  • Figure 2: Measurement temperature (top), FET temperature (middle), and absolute humidity (bottom) over all 1 852 measurements ordered according to their IDs.
  • Figure 3: Empirical cumulative distribution functions of recorded absolute humidities for adipose (solid, blue line) and muscle tissue samples (dashed, black line) with dotted lines indicates boundaries of 95% confidence intervals.
  • Figure 4: Average dispersion plot for adipose tissue (top) and corresponding CV/SV-wise standard deviations (middle), as well as the differences between the average responses and threefold standard deviations (bottom). Responses in pA.
  • Figure 5: Average dispersion plot for muscle tissue (top) and corresponding CV/SV-wise standard deviations (middle), as well as the differences between the average responses and threefold standard deviations (bottom). Responses in pA.
  • ...and 4 more figures