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Related Experiment Video

Updated: Nov 27, 2025

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Published on: March 9, 2018

Detecting multiple sclerosis via breath analysis using an eNose, a pilot study.

A R Ettema¹, M W P M Lenders¹, J Vliegen¹

¹Department of Neurology and Neurosurgery, Medisch Spectrum Twente, Enschede, The Netherlands.

Journal of Breath Research

|December 3, 2020

Summary

This summary is machine-generated.

An electronic nose (eNose) shows potential for detecting multiple sclerosis (MS) by analyzing breath. The Aeonose device distinguished MS patients from controls, suggesting a possible quick screening tool for MS diagnosis.

Keywords:

electronic nose exhaled breath analysis multiple sclerosis (MS)volatile organic compounds (VOCs)

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Breath Collection from Children for Disease Biomarker Discovery

Breath Collection from Children for Disease Biomarker Discovery

Published on: February 14, 2019

Area of Science:

Neurology
Biomedical Engineering
Analytical Chemistry

Background:

Multiple sclerosis (MS) is a chronic neurological disease.
Current diagnostic methods can be invasive and time-consuming.
Non-invasive diagnostic tools are needed for early detection and screening.

Purpose of the Study:

To investigate the potential of an electronic nose (eNose) for detecting multiple sclerosis (MS) through exhaled breath analysis.
To evaluate the diagnostic performance of the Aeonose device in distinguishing MS patients from healthy controls.

Main Methods:

A prospective, non-invasive study involving 124 MS patients and 129 healthy controls.
Exhaled breath samples were collected using the Aeonose eNose device.
Artificial neural network (ANN) models were trained to analyze volatile organic compound (VOC) patterns.

Main Results:

The ANN model using the entire dataset achieved a sensitivity of 0.75 and specificity of 0.60 in distinguishing MS patients.
A model trained on non-medicated MS patients and controls showed improved performance with a sensitivity of 0.93 and specificity of 0.74.
The Aeonose device demonstrated the ability to differentiate between MS patients and healthy individuals.

Conclusions:

The Aeonose eNose shows promise as a non-invasive screening tool for multiple sclerosis.
Breath analysis using eNose technology could potentially aid in the early detection of MS.
Further research is required to validate its efficacy in differentiating new MS cases.