Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

When AI and Experts Agree on Error: Intrinsic Ambiguity in Dermatoscopic Images.

Journal of imaging·2026
Same author

Oncometabolite signatures from tumor-stroma crosstalk as potential non-invasive biomarkers.

Cell death discovery·2026
Same author

Sparse Temporal AutoEncoder for ECG Anomaly Detection.

Sensors (Basel, Switzerland)·2026
Same author

Gated Attention-Augmented Double U-Net for White Blood Cell Segmentation.

Journal of imaging·2025
Same author

Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.

Sensors (Basel, Switzerland)·2025
Same author

An Innovative IoT and Edge Intelligence Framework for Monitoring Elderly People Using Anomaly Detection on Data from Non-Wearable Sensors.

Sensors (Basel, Switzerland)·2025

Related Experiment Video

Updated: Apr 23, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

826

An efficient approach for preprocessing data from a large-scale chemical sensor array.

Marco Leo1, Cosimo Distante2, Mara Bernabei3

  • 1National Research Council of Italy, Institute of Optics, via della Libertà 3 Arnesano (Lecce), 73010, Italy. marco.leo@ino.it.

Sensors (Basel, Switzerland)
|September 26, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an artificial olfactory system (Electronic Nose) using a large chemical sensor array and advanced software for data processing. The system effectively normalizes sensor data, reduces noise, and classifies pure compounds and binary mixtures.

More Related Videos

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.5K
PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

11.8K

Related Experiment Videos

Last Updated: Apr 23, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

826
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.5K
PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis

Published on: May 11, 2017

11.8K

Area of Science:

  • Artificial intelligence
  • Chemical sensing
  • Sensor technology

Background:

  • Biological olfaction is complex and challenging to replicate.
  • Existing artificial olfactory systems often struggle with data processing and noise reduction.

Purpose of the Study:

  • To introduce a novel artificial olfactory system (Electronic Nose).
  • To detail the software components for advanced data processing, including normalization and data reduction.
  • To validate the system's performance in classifying pure analytes and binary mixtures.

Main Methods:

  • Development of a Large-Scale Chemical Sensor Array with 384 sensors made of 24 conducting polymer materials.
  • Implementation of software modules for data normalization, noise reduction, and dimensionality reduction.
  • Utilizing a classification task to recognize pure compounds and predict binary mixture concentrations.

Main Results:

  • The software successfully normalized heterogeneous sensor data and reduced inherent noise.
  • Data reduction techniques extracted informative directions for efficient lower-dimensional representation.
  • Experimental analysis demonstrated the system's capability in analyzing pure analytes and binary mixtures, including a classification task.

Conclusions:

  • The developed artificial olfactory system shows promise for accurate odor analysis and classification.
  • The advanced data processing software is crucial for the system's effective performance.
  • The system can potentially recognize pure compounds and predict binary mixture concentrations.