Evidence map›Paper›PMID 36994078›Full record

ArticleiScience2023

The use of novel electronic nose technology to locate missing persons for criminal investigations.

Amber Brown, Erin Lamb, Alisha Deo, Daniel Pasin, Taoping Liu, Wentian Zhang, Steven Su, Maiken Ueland

Abstract read
In one paragraph

Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 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
  3. Article
  4. Article
  5. Review
  6. 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.

Amber BrownCentre for Forensic Sciences, School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW 2007, Australia.
Erin LambCentre for Forensic Sciences, School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW 2007, Australia.
Alisha DeoCentre for Forensic Sciences, School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW 2007, Australia.
Daniel PasinCentre for Forensic Sciences, School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW 2007, Australia.
Taoping LiuFaculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
Wentian ZhangFaculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
Steven SuFaculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
Maiken UelandCentre for Forensic Sciences, School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW 2007, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The search for missing persons is a major challenge for investigations involving presumed deceased individuals. Currently, the most effective tool is the use of cadaver-detection dogs; however, they are limited by their cost, limited operation times, and lack of granular information reported to the handler. Thus, there is a need for discrete, real-time detection methods that provide searchers explicit information as to whether human-decomposition volatiles are present. A novel e-nose (NOS.E) developed in-house was investigated as a tool to detect a surface-deposited individual over time. The NOS.E was able to detect the victim throughout most stages of decomposition and was influenced by wind parameters. The sensor responses from different chemical classes were compared to chemical class abundance confirmed by two-dimensional gas chromatography - time-of-flight mass spectrometry. The NOS.E demonstrated its ability to detect surface-deposited individuals days and weeks since death, demonstrating its utility as a detection tool.

Indexed as

Health technology

Identifiers

PMID36994078
PMCPMC10040731

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.