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

Animal-Origin Food Waste Across Global Supply Chains: Trends, Upcycling Strategies, and Circular Economy Solutions.

Foods (Basel, Switzerland)·2026
Same author

Stable and reliable method for simultaneous quantification of five neonicotinoid residues in plant-derived foods.

Food chemistry·2026
Same author

Plantain seed mucilage-derived aerogel for microplastics removal in aged traditional Chinese spirit.

Food chemistry·2026
Same author

De Novo Design of Membrane-Targeting Antimicrobial Peptides Against Gram-Negative Bacteria Using a Generative Artificial Intelligence Framework.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Green enzyme-ultrasound-membrane extraction of rapeseed protein: Enhancing purity and reducing antinutritional factors.

Food chemistry·2026
Same author

High-voltage electrostatic field technology in food processing and preservation: mechanisms, applications, and emerging innovations.

Food chemistry·2026

Related Experiment Video

Updated: Jul 5, 2025

Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water
12:50

Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water

Published on: June 9, 2014

14.6K

Visible detection of chilled beef freshness using a paper-based colourimetric sensor array combining with deep

Yuandong Lin1, Ji Ma1, Jun-Hu Cheng1

  • 1School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China.

Food Chemistry
|January 17, 2024
PubMed
Summary

This study introduces a colorimetric sensor array and deep learning for rapid, accurate chilled beef freshness assessment. The innovative method effectively detects amine gases and monitors beef quality with high precision.

Keywords:
Chilled beefColourimetric sensor arrayDeep learningFood freshnessFood storage

More Related Videos

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out
07:10

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Published on: November 9, 2018

9.4K
Tracking Microbial Contamination in Retail Environments Using Fluorescent Powder - A Retail Delicatessen Environment Example
05:49

Tracking Microbial Contamination in Retail Environments Using Fluorescent Powder - A Retail Delicatessen Environment Example

Published on: March 5, 2014

11.1K

Related Experiment Videos

Last Updated: Jul 5, 2025

Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water
12:50

Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water

Published on: June 9, 2014

14.6K
Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out
07:10

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Published on: November 9, 2018

9.4K
Tracking Microbial Contamination in Retail Environments Using Fluorescent Powder - A Retail Delicatessen Environment Example
05:49

Tracking Microbial Contamination in Retail Environments Using Fluorescent Powder - A Retail Delicatessen Environment Example

Published on: March 5, 2014

11.1K

Area of Science:

  • Food Science and Technology
  • Analytical Chemistry
  • Artificial Intelligence in Food Safety

Background:

  • Assessing chilled beef freshness is crucial for food safety and quality control.
  • Traditional methods for determining beef freshness can be time-consuming and subjective.
  • Amine gases are key indicators of spoilage in meat products.

Purpose of the Study:

  • To develop an innovative approach for detecting amine gases and assessing chilled beef freshness.
  • To integrate a colorimetric sensor array (CSA) with advanced algorithms, including deep learning.
  • To achieve rapid, robust, and accurate monitoring of chilled beef quality.

Main Methods:

  • Development of a colorimetric sensor array (CSA) using twelve pH-response dyes.
  • Application of multivariate statistical analysis for distinguishing amine gases and quantifying trimethylamine.
  • Utilization of deep learning models (ResNet34, VGG16, GoogleNet) for freshness evaluation, with t-SNE for visualization.

Main Results:

  • The CSA effectively distinguished five amine gases with a limit of detection (LOD) of 8.02 ppb for trimethylamine.
  • Deep learning models achieved a 98.0% overall accuracy in assessing chilled beef freshness.
  • t-distributed stochastic neighbour embedding (t-SNE) provided insights into the deep learning classification process.

Conclusions:

  • The combined approach of CSA and deep learning offers a rapid and accurate method for chilled beef freshness assessment.
  • This technology enables visual monitoring and precise quantification of spoilage indicators.
  • Deep learning significantly enhances pattern recognition for reliable food quality evaluation.