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

Author Correction: Photothermal effects control ultrafast charge transport in titanium carbide MXenes.

Nature communications·2026
Same author

A novel strategy of "Separation Surgery Combined with Vertebroplasty and Interstitial Implantation of <sup>125</sup>I Seeds (SSVPI)" in managing thoracic metastases from lung adenocarcinoma with spinal cord compression.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2026
Same author

Stage-Specific Expression and Perinuclear Enrichment of γ-Tubulin During Tomont Development and Theront Morphogenesis in Cryptocaryon irritans.

The Journal of eukaryotic microbiology·2026
Same author

Correction: The role of gut microbiota mediated ferroptosis in PCOS and the therapeutic potential of Chinese herbal medicine.

Frontiers in medicine·2026
Same author

Mixed-species afforestation stimulates the flow and turnover of carbon and nitrogen within soil aggregates in a degraded karst ecosystem.

Journal of environmental management·2026
Same author

Exosomal miR-4644 Targets SPRY3 to Promote Proliferation and Invasion of Pancreatic Cancer.

Cancer medicine·2026

Related Experiment Video

Updated: Dec 29, 2025

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
08:46

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

8.1K

Electronic Tongue Recognition with Feature Specificity Enhancement.

Tao Liu1, Yanbing Chen1, Dongqi Li1

  • 1School of Microelectronics and Communication Engineering, Chongqing University, No. 174 Shazheng Street, Shapingba District, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|February 7, 2020
PubMed
Summary

A new feature extraction method, feature specificity enhancement (FSE), improves electronic tongue (E-tongue) performance by reducing common signals. Combined with kernel extreme learning machine (KELM), it enhances liquid analysis accuracy and efficiency.

Keywords:
electronic tonguefeature extractionkernel extreme learning machinespecificity enhancement

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.1K
Bacterial Detection & Identification Using Electrochemical Sensors
09:30

Bacterial Detection & Identification Using Electrochemical Sensors

Published on: April 23, 2013

28.9K

Related Experiment Videos

Last Updated: Dec 29, 2025

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis
08:46

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

8.1K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.1K
Bacterial Detection & Identification Using Electrochemical Sensors
09:30

Bacterial Detection & Identification Using Electrochemical Sensors

Published on: April 23, 2013

28.9K

Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Machine Learning

Background:

  • Electronic tongues (E-tongues) use sensor arrays and machine learning for liquid analysis.
  • Large amplitude pulse voltammetry (LAPV) E-tongues generate high-frequency data, necessitating efficient feature extraction.
  • Common-mode signals in sensor arrays can hinder machine learning model performance.

Purpose of the Study:

  • To develop a fast and effective feature extraction method for LAPV E-tongue data.
  • To enhance feature specificity and reduce dimensionality for improved liquid analysis.
  • To evaluate the performance of the proposed method against existing approaches.

Main Methods:

  • Proposed feature specificity enhancement (FSE) to eliminate common mode signals and highlight specific sensor responses.
  • Utilized radial basis function for nonlinear feature projection.
  • Employed kernel extreme learning machine (KELM) for rapid and flexible pattern recognition.
  • Evaluated models on two LAPV E-tongue datasets: beverage identification and a public benchmark.

Main Results:

  • The FSE method effectively enhances feature specificity and reduces data dimensionality.
  • The combination of FSE and KELM demonstrated superior accuracy compared to other models.
  • The proposed FSE-KELM model showed reduced time consumption and memory costs.
  • The model exhibited low parameter sensitivity, indicating robustness.

Conclusions:

  • Feature specificity enhancement (FSE) coupled with kernel extreme learning machine (KELM) offers a powerful approach for E-tongue data analysis.
  • This method significantly improves recognition performance and computational efficiency.
  • The FSE-KELM model is a promising tool for advanced liquid analysis applications.