Evidence map›Paper›PMID 39338662›Full record

ArticleSensors (Basel, Switzerland)2024

Performance of a Novel Electronic Nose for the Detection of Volatile Organic Compounds Relating to Starvation or Human Decomposition Post-Mass Disaster.

Emily J Sunnucks, Bridget Thurn, Amber O Brown, Wentian Zhang, Taoping Liu, Shari L Forbes, Steven Su, Maiken Ueland

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Adaptive Machine Learning for Electronic Nose-Based Forensic VOC Classification.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Emily J SunnucksCentre for Forensic Sciences, School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia.ORCID 0009-0006-3326-854X
Bridget ThurnCentre for Forensic Sciences, School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia.ORCID 0000-0002-0787-1672
Amber O BrownCentre for Forensic Sciences, School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia.ORCID 0000-0002-6761-2170
Wentian ZhangFaculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.ORCID 0000-0002-2064-0326
Taoping LiuAcademy of Advanced Interdisciplinary Research, Xidian University, Xi'an 710071, China.ORCID 0000-0003-3827-7489
Shari L ForbesDepartment of Chemistry and Biochemistry, University of Windsor, Windsor, ON N9B 3P4, Canada.ORCID 0000-0001-7416-0080
Steven SuFaculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Maiken UelandCentre for Forensic Sciences, School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia.ORCID 0000-0002-9155-3502

Funding

Australian Research Council DE210100494
6 · The paper itself

Abstract

There has been a recent increase in the frequency of mass disaster events. Following these events, the rapid location of victims is paramount. Currently, the most reliable search method is scent detection dogs, which use their sense of smell to locate victims accurately and efficiently. Despite their efficacy, they have limited working times, can give false positive responses, and involve high costs. Therefore, alternative methods for detecting volatile compounds are needed, such as using electronic noses (e-noses). An e-nose named the 'NOS.E' was developed and has been used successfully to detect VOCs released from human remains in an open-air environment. However, the system's full capabilities are currently unknown, and therefore, this work aimed to evaluate the NOS.E to determine the efficacy of detection and expected sensor response. This was achieved using analytical standards representative of known human ante-mortem and decomposition VOCs. Standards were air diluted in Tedlar gas sampling bags and sampled using the NOS.E. This study concluded that the e-nose could detect and differentiate a range of VOCs prevalent in ante-mortem and decomposition VOC profiles, with an average LOD of 7.9 ppm, across a range of different chemical classes. The NOS.E was then utilized in a simulated mass disaster scenario using donated human cadavers, where the system showed a significant difference between the known human donor and control samples from day 3 post-mortem. Overall, the NOS.E was advantageous: the system had low detection limits while offering portability, shorter sampling times, and lower costs than dogs and benchtop analytical instruments.

Indexed as

Electronic NoseVolatile Organic CompoundsAnimalsDisastersHumansOdorantsVolatile Organic Compoundsdecomposition odorDVImachine olfactionmetal oxide sensorssensorVOCs

Identifiers

PMID39338662
PMCPMC11435962

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.