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

Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

You might also read

Related Articles

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

Sort by
Same author

Enhancing acid stability of phycocyanin via non-covalent interactions with gelatin matrices of different Bloom values.

Food chemistry·2026
Same author

Machine learning-driven discovery of antimicrobial peptides against <i>Pseudomonas aeruginosa</i>.

Frontiers in pharmacology·2026
Same author

A multi-objective optimization framework for planning electric vehicle charging infrastructure incorporating traffic demand, cost, and equity considerations.

Scientific reports·2026
Same author

Depression and risk of incident heart diseases among older adults at CKM stages 0-3: evidence from the China Health and Retirement Longitudinal Study.

Annals of medicine·2026
Same author

High-Accuracy Prediction of Chunmee Tea Grade via DeepSpectra Model and Near-Infrared Spectroscopy.

Foods (Basel, Switzerland)·2026
Same author

Phase measurement and compensation method for a waveguide beam based on an on-chip 90° optical hybrid mixer.

Optics express·2026

Related Experiment Video

Updated: Jun 19, 2026

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

Machine Learning Algorithms Enabled Visual On-Site Intelligent Sensing of Bioactive Components by

Zemin Ren1, Yatong Zhang1, Feifei Xu1

  • 1Tianjin Key Laboratory of Industrial Microbiology, College of Biotechnology, Tianjin University of Science and Technology, No. 29 of 13th Street, TEDA, Tianjin 300457, PR China.

ACS Sensors
|June 18, 2026
PubMed
Summary

This study introduces a novel histidine-functionalized nanozyme for enhanced detection of bioactive components (BCs) in food. Machine learning integration achieved 100% precision in identifying BCs, improving food safety and health analysis.

Keywords:
TA-Cu-His nanozymebioactive componentslaccase-likemachine learningsensor array

More Related Videos

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics
10:50

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics

Published on: July 16, 2018

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Related Experiment Videos

Last Updated: Jun 19, 2026

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

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics
10:50

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics

Published on: July 16, 2018

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

  • Biochemistry
  • Materials Science
  • Analytical Chemistry

Background:

  • Identifying bioactive components (BCs) in food is crucial for understanding health benefits.
  • Existing nanozyme sensor arrays for BC detection need performance improvements for practical applications.
  • Developing highly active nanozymes is key to enhancing sensor array performance and biosensing capabilities.

Purpose of the Study:

  • To develop an advanced nanozyme with superior catalytic activity for improved BC detection.
  • To leverage machine learning and pH-dependent activity for precise BC identification.
  • To create an intelligent sensing platform for efficient and practical BC analysis.

Main Methods:

  • A histidine-functionalized trimesic acid-copper (TA-Cu-His) nanozyme was synthesized using defect engineering.
  • Recursive Feature Elimination (RFE) was used to select optimal pH sensing channels.
  • Machine learning algorithms, including ResNet-50, were integrated with the sensor array for data analysis.

Main Results:

  • The TA-Cu-His nanozyme exhibited enhanced laccase-like (LAC) activity compared to the original TA-Cu material.
  • The sensor array achieved 100% precision in classifying BCs, a significant improvement from 58.62%.
  • Blind samples were successfully recognized, demonstrating the system's robustness.

Conclusions:

  • The developed TA-Cu-His nanozyme offers enhanced catalytic activity for biosensing applications.
  • The integration of RFE and ML algorithms enables highly accurate and efficient identification of multiple BCs.
  • This work presents a novel strategy for intelligent and practical BC identification using ML-assisted sensing platforms.