Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Shyness and academic procrastination among Chinese adolescents: a moderated mediation model of self-regulation and self-focused attention.

Frontiers in psychology·2024

Same author

Regulation of ferroptosis by PI3K/Akt signaling pathway: a promising therapeutic axis in cancer.

Frontiers in cell and developmental biology·2024

Same author

Biosynthesis of Scopoletin in Sweet Potato Confers Resistance against <i>Fusarium oxysporum</i>.

Journal of agricultural and food chemistry·2024

Same author

Study on the Thermal Stability of the Sweet-Tasting Protein Brazzein Based on Its Structure-Sweetness Relationship.

Journal of agricultural and food chemistry·2024

Same author

A novel bHLH gene responsive to low nitrogen positively regulates the biosynthesis of medicinal tropane alkaloids in Atropa belladonna.

International journal of biological macromolecules·2024

Same author

Ultralow-loss optical interconnect enabled by topological unidirectional guided resonance.

Science advances·2024

Search research articles

Related Experiment Video

Updated: Apr 7, 2026

Fruit Volatile Analysis Using an Electronic Nose

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose

Xiuzhen Guo¹, Chao Peng², Songlin Zhang³

¹College of Electronics and Information Engineering, Southwest University, Chongqing 400715, China. swugxz@163.com.

Sensors (Basel, Switzerland)

|July 2, 2015

Summary

A new method called moving window function capturing (MWFC) enhances electronic nose (E-nose) accuracy for detecting pathogens. Optimizing sensor arrays and models with QPSO-SVM improves classification performance.

Keywords:

MWFC QPSO SVM electronic nose feature extraction

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Sampling and Analysis of Animal Scent Signals

Sampling and Analysis of Animal Scent Signals

Published on: February 13, 2021

Related Experiment Videos

Last Updated: Apr 7, 2026

Fruit Volatile Analysis Using an Electronic Nose

Fruit Volatile Analysis Using an Electronic Nose

Published on: March 30, 2012

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Sampling and Analysis of Animal Scent Signals

Sampling and Analysis of Animal Scent Signals

Published on: February 13, 2021

Area of Science:

Biomedical Engineering
Computational Biology
Sensor Technology

Background:

Electronic noses (E-noses) are crucial for detecting pathogens.
Accurate feature extraction is vital for E-nose performance.
Existing methods may lack optimal sensitivity for complex biological samples.

Purpose of the Study:

To introduce a novel feature extraction technique, moving window function capturing (MWFC).
To enhance the accuracy of E-nose systems for infectious pathogen detection in rat wound models.
To optimize sensor array and model parameters using a quantum-behaved particle swarm optimization (QPSO) algorithm with support vector machine (SVM).

Main Methods:

Developed and applied the moving window function capturing (MWFC) method for E-nose signal analysis.
Integrated QPSO with SVM for simultaneous optimization of sensor array and classification model parameters.
Tested the proposed method on E-nose data from rat wound samples infected with pathogens.

Main Results:

The MWFC approach demonstrated superior feature extraction capabilities for E-nose signals.
The QPSO-SVM optimization led to significantly higher classification accuracy compared to established techniques.
Variations in window parameters (type, width, position) influenced classification outcomes, highlighting the method's adaptability.

Conclusions:

The proposed MWFC method is effective for E-nose feature extraction, improving pathogen detection accuracy.
Optimizing window functions can enhance E-nose performance, offering a tunable approach.
The QPSO-SVM integration provides a robust framework for E-nose system calibration and performance enhancement.