Predicting lecithin concentration from differential mobility spectrometry measurements with linear regression models and neural networks

Anna Anttalainen, Meri Mäkelä, Pekka Kumpulainen, Antti Vehkaoja, Osmo Anttalainen, Niku Oksala, Antti Roine

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

Differential mobility spectrometry (DMS) analysis of electrosurgical smoke can be used to distinguish cancerous and healthy tissues. Mass spectrometry studies of surgical smoke have revealed phospholipids as the key compounds enabling this discrimination. Lecithin is a mixture of phospholipids encountered in tissues. We hypothesized that DMS is capable of detecting and quantifying lecithin from water solution in headspace chamber, paving way for analysis of surgical smoke. We measured different lecithin concentrations in a biologically relevant range considering healthy and cancerous tissues with DMS and trained regression models to predict the analyte concentration. The models were internally cross-validated and externally validated. The best cross-validation results were obtained with convolutional neural networks, with root mean square error (RMSE) = 0.38 mg/ml. This is the first demonstration of estimation of analyte concentration from DMS measurements with neural networks. The best external validation results were acquired with sparse linear regression methods, with RMSE varying from 0.40 mg/ml to 0.41 mg/ml. The results demonstrate that DMS is sufficiently sensitive to detect biologically relevant changes in phospholipid concentration, potentially explaining its ability to detect cancerous tissue. In the future, we aim to reproduce the results by using surgical smoke as the medium. In this scenario, the complex background of surgical smoke will be the main challenge to overcome. Predicting concentration with neural networks also lays the foundation for wider analytical usage of DMS.

Original languageEnglish
Article number121926
JournalTalanta
Volume225
Early online date1 Dec 2020
DOIs
Publication statusPublished - 1 Apr 2021
Publication typeA1 Journal article-refereed

Keywords

  • Concentration prediction
  • Differential mobility spectrometry
  • Lecithin
  • Neural networks
  • Phospholipids
  • Regression analysis

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Analytical Chemistry

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