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The role of the detectors in High-Performance Liquid Chromatography (HPLC) is to analyze the solutes as they exit from the chromatographic column. The detector recognizes the solute's property and generates corresponding electrical signals, which are converted into a readable graph of the detector's response versus elution time called a chromatogram at the computer. There are several types of HPLC detectors, each with its own advantages and limitations, depending on the analyte...
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Fruit Volatile Analysis Using an Electronic Nose
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Adaptive Machine Learning for Electronic Nose-Based Forensic VOC Classification.

Ivan Shtepliuk1, Kerstin Montelius2, Jens Eriksson1

  • 1Department of Physics, Chemistry and Biology, Linköping University, Linköping, 581 83, Sweden.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 24, 2025
PubMed
Summary
This summary is machine-generated.

An AI-powered electronic nose offers a novel forensic tool for odor detection. This technology accurately identifies human remains and estimates time since death, providing a scalable alternative to traditional methods.

Keywords:
artificial olfactory sensor systemelectronic nose (e‐nose)machine learningodor‐based forensic analysispostmortem interval estimationvolatile organic compounds (VOCs)

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Area of Science:

  • Forensic Science
  • Biotechnology
  • Artificial Intelligence

Background:

  • Odor detection is an emerging forensic technique for individual identification when conventional methods fail.
  • Challenges include distinguishing human remains, estimating postmortem intervals, and differentiating living from deceased individuals.
  • Trained dogs are effective but limited; faster, scalable alternatives are needed.

Purpose of the Study:

  • To present a bio-inspired electronic nose integrated with machine learning as a non-invasive tool for forensic scent detection.
  • To evaluate the system's accuracy in classifying human biosamples and estimating postmortem intervals.
  • To explore AI-driven olfactory platforms as alternatives to traditional forensic methods.

Main Methods:

  • Development of a bio-inspired electronic nose with 32 metal-oxide sensors.
  • Integration of machine learning algorithms for volatile organic compound (VOC) profiling.
  • Testing the system on postmortem versus antemortem human biosamples and human versus animal tissue.

Main Results:

  • Accurate classification of postmortem versus antemortem human biosamples (98.1%).
  • Effective discrimination between human and animal tissue (97.2%).
  • High temporal resolution in estimating postmortem intervals.

Conclusions:

  • The AI-driven olfactory platform provides a reliable, real-time tool for forensic scent detection.
  • This technology can complement or replace traditional forensic identification methods.
  • The approach advances next-generation forensic diagnostics and odor-based biomarker discovery.