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

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...

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

Protocol for natural and biomedical image processing in the hypercomplex domain using the 2D orthogonal planes split.

STAR protocols·2026
Same author

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

Food chemistry·2026

Related Experiment Video

Updated: Jun 14, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

Supervised neural network classification of pre-sliced cooked pork ham images using quaternionic singular values.

Nektarios A Valous1, Fernando Mendoza, Da-Wen Sun

  • 1FRCFT Group, Biosystems Engineering, Agriculture and Food Science Centre, School of Agriculture Food Science and Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland.

Meat Science
|April 9, 2010
PubMed
Summary

Quaternionic singular value decomposition effectively classifies pork ham images. This method uses singular values as features for artificial neural network classification, achieving high accuracy for visually similar samples.

More Related Videos

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Related Experiment Videos

Last Updated: Jun 14, 2026

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Image processing
  • Machine learning
  • Food science

Background:

  • Color images can be represented as quaternion matrices.
  • Quaternionic singular value decomposition (QSVD) extracts useful properties from these matrices.
  • Classifying visually similar food items like pork ham is challenging.

Purpose of the Study:

  • To utilize QSVD for robust feature extraction.
  • To classify sliced pork ham images based on quality.
  • To evaluate the performance of an artificial neural network classifier.

Main Methods:

  • QSVD was applied to quaternion matrices representing pork ham images.
  • Feature selection used Mahalanobis distances and Pearson correlations.
  • An adaptive feedforward multilayer perceptron was trained with selected features.

Main Results:

  • Six highly discriminating features were identified.
  • The neural network achieved high classification accuracy: 90.3% (training), 94.4% (validation), and 86.1% (test).
  • QSVD enabled recognition of subtle textural differences in similar-looking pork ham.

Conclusions:

  • QSVD is a promising technique for image classification tasks.
  • Feature extraction using QSVD improves classification of visually similar food products.
  • The study demonstrates satisfactory classification performance for pork ham quality.