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

Observational Learning01:12

Observational Learning

250
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
250
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

410
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
410
Introduction to Learning01:18

Introduction to Learning

492
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
492

You might also read

Related Articles

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

Sort by
Same author

Impact of Creatine Supplementation on the Quality and Storage Characteristics of Emulsified Pork Sausages.

Food science of animal resources·2026
Same author

Impact of Sous-vide Cooking on Quality Attributes of High-Fat and Low-Fat Cuts of Beef, Pork, and Chicken.

Food science of animal resources·2026
Same author

Citrus sunki Peel Extract Enhances Proliferation and Differentiation of Fibro-Adipocyte Progenitors in Holstein Cattle for Cultivated Meat Production.

Food science of animal resources·2026
Same author

Quality and Storage Characteristics of Hanwoo Pemmican by Replacing Canola Oil.

Food science of animal resources·2026
Same author

Early essential newborn care: a decade of scaling up quality, life-saving interventions across East Asia and the Pacific (2013-2023).

BMJ global health·2026
Same author

Effects of Magnolia denudata extract on quality and storage characteristics of emulsified chicken sausage.

Food science of animal resources·2026

Related Experiment Video

Updated: Aug 3, 2025

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

459

A deep learning-based framework for predicting pork preference.

Eunyoung Ko1, Kyungchang Jeong2, Hongseok Oh2

  • 1Dodram Pig Farmers Cooperative Company, Icheon, 17405, Republic of Korea.

Current Research in Food Science
|April 7, 2023
PubMed
Summary
This summary is machine-generated.

South Korean pork consumption is rising, with consumers preferring high-fat cuts like pork belly. This study uses deep learning and ultrasound data to predict consumer flavor and appearance preferences for pork.

Keywords:
Artificial intelligenceConsumer preference predictionDeep learningPork qualityPrecision livestock farming (PLF)

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Fat Preference: A Novel Model of Eating Behavior in Rats
05:57

Fat Preference: A Novel Model of Eating Behavior in Rats

Published on: June 27, 2014

13.3K

Related Experiment Videos

Last Updated: Aug 3, 2025

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

459
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Fat Preference: A Novel Model of Eating Behavior in Rats
05:57

Fat Preference: A Novel Model of Eating Behavior in Rats

Published on: June 27, 2014

13.3K

Area of Science:

  • Food Science
  • Agricultural Technology
  • Consumer Behavior

Background:

  • Meat consumption, particularly pork, is increasing in South Korea.
  • Consumers show a strong preference for high-fat pork cuts, such as pork belly.
  • Meeting consumer demand for specific pork characteristics is crucial for market competitiveness.

Purpose of the Study:

  • To develop a deep learning framework for predicting consumer preference scores for pork flavor and appearance.
  • To utilize ultrasound equipment for objective pork characteristic assessment.
  • To establish a predictive model linking pork traits to consumer preferences.

Main Methods:

  • Collected pork characteristic data using ultrasound equipment (AutoFom III).
  • Investigated and recorded consumer preferences for pork flavor and appearance over an extended period.
  • Applied a deep neural network-based ensemble technique for preference score prediction.

Main Results:

  • Demonstrated a strong correlation between predicted preference scores and objective pork belly characteristics.
  • Validated the proposed deep learning framework through empirical evaluation and consumer surveys.
  • The study successfully predicted consumer preferences based on pork quality attributes.

Conclusions:

  • The deep learning framework effectively predicts consumer preferences for pork.
  • Ultrasound technology combined with AI can guide pork production to meet consumer demands.
  • This approach offers a competitive advantage in managing and marketing pork products.