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

Methods of Classification and Identification

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by

Same author

Stochastic Grey Wolf Optimization for Hyperparameter Tuning of LSTM and RNN Models in Energy Forecasting.

Scientific reports·2026

Same author

Veterinary fracture diagnosis: a deep learning model for dogs long bone fractures.

Scientific reports·2026

Same author

Hybrid deep learning and feature selection approach for autism detection from rs-fMRI data.

PloS one·2026

Same author

Aerial image segmentation using multilevel thresholding based on multi strategy Osprey optimization algorithm.

Scientific reports·2026

Same author

Enhancing particle swarm optimization based on optical computing mechanism: application to dyslexia detection.

Frontiers in artificial intelligence·2026

Same author

A deep residual 1D-CNN with self-attention for fraud transaction detection in virtual economies.

Scientific reports·2026

Same journal

Aggregate status determining the foaming behavior of phycocyanin: insights from bulk phase and air-water interface structure.

Food chemistry·2026

Same journal

Phenolipids: natural origins, synthesis, bioactivity, and functional relevance in food systems.

Food chemistry·2026

Same journal

Characterization and preparation of Litsea cubeba essential oil microcapsules encapsulated by modified maltodextrin prepared under the guidance of dynamic changes in protein structure.

Food chemistry·2026

Same journal

Cold plasma treatment of noni juice: A dual strategy for flavor improvement and bioactivity enhancement.

Food chemistry·2026

Same journal

Metabolomic insights into flavonoid migration and bioactivity enhancement in Apocynum venetum tea during hot-water brewing.

Food chemistry·2026

Same journal

Molecular crosstalk of probiotics in gut health: A chemical perspective on metabolite-mediated pathways.

Food chemistry·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 19, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Detection and classification of meat freshness using an optimized deep learning method.

Mohamed Abd Elfattah¹, Ahmed A Ewees², Ashraf Darwish³

¹Computer Science Department, Misr Higher Institute for Commerce and Computers, Mansoura 35511, Egypt.

|June 6, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning method for classifying meat freshness, achieving 98.51% accuracy. This approach enhances food safety and quality control in the food industry.

Keywords:

Artificial protozoa optimizer Deep learning Feature selection Meat quality assessment Optimization VGG19

More Related Videos

Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

Published on: September 20, 2016

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Sep 19, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

Published on: September 20, 2016

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Food Science
Computer Science
Artificial Intelligence

Background:

Accurate meat freshness assessment is vital for food safety and waste reduction.
Efficient classification of meat (fresh, half-fresh, spoiled) benefits producers, retailers, and consumers.

Purpose of the Study:

To develop a deep learning-based method for accurate meat freshness classification.
To improve food safety monitoring systems through automated classification.

Main Methods:

Feature extraction using Visual Geometry Group 19 (VGG19) convolutional neural network.
Feature selection via Improved Artificial Protozoa Optimizer (IAPO) with Particle Swarm Optimization (PSO).
Classification using the optimized feature set.

Main Results:

The proposed method achieved 98.51% accuracy, 98.54% sensitivity, and 99.24% specificity.
Outperformed five other established optimization techniques in benchmarking tests.

Conclusions:

The deep learning method demonstrates high efficacy for meat freshness classification.
The approach shows potential for integration into food safety monitoring systems.