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

Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

You might also read

Related Articles

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

Sort by
Same author

The role of MLO in powdery mildew susceptibility depends on a combination of functional specialization and subcellular localization.

Plant physiology·2026
Same author

Negative modulation of maternal iodine deficiency and excess on milk lipid synthesis and secretion in lactating rats.

The Journal of nutritional biochemistry·2025
Same author

An Ultra-Robust, Highly Compressible Silk/Silver Nanowire Sponge-Based Wearable Pressure Sensor for Health Monitoring.

Biosensors·2025
Same author

BioFuse: A programmable timer switch of gene expression.

Science advances·2025
Same author

The interaction between McMLO7b and McCASPL22 regulating the susceptibility to powdery mildews in balsam pear.

Plant physiology and biochemistry : PPB·2025
Same author

Predicting Anti-Cancer Drug Response Based on Hypergraph Representation Learning.

IEEE transactions on computational biology and bioinformatics·2025

Related Experiment Video

Updated: Jul 9, 2026

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

Integrating Vision-Language Models for Accelerated High-Throughput Nutrition Screening.

Peihua Ma1, Yixin Wu2, Ning Yu3

  • 1Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, MD, 20742, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|July 8, 2024
PubMed
Summary

This study introduces vision-language models (VLMs) for faster, more accurate food nutritional analysis. The new method significantly speeds up nutrient estimation while maintaining high precision, aiding healthcare and the food industry.

Keywords:
food analysishigh‐throughput screeningmachine learningprecision nutritionvision‐language model

More Related Videos

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq
04:54

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

Published on: March 19, 2021

4.6K
Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
06:21

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

5.7K

Related Experiment Videos

Last Updated: Jul 9, 2026

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq
04:54

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

Published on: March 19, 2021

4.6K
Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
06:21

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

5.7K

Area of Science:

  • Food Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Accurate nutritional profiling is crucial for healthcare and the food industry.
  • Traditional methods for nutritional analysis can be time-consuming and lack high throughput.
  • Integrating advanced computational models with chemical analysis offers potential for improved efficiency.

Purpose of the Study:

  • To pioneer the integration of vision-language models (VLMs) with chemical analysis for nutritional profiling.
  • To develop a cutting-edge VLM for enhanced speed and accuracy in nutrient estimation.
  • To establish a new benchmark in the precision of nutritional data compilation.

Main Methods:

  • Utilized the UMDFood-90k database with a novel vision-language model (VLM).
  • Integrated VLM with traditional chemical analysis techniques for validation.
  • Evaluated model performance using macro-AUCROC for lipid quantification.

Main Results:

  • Achieved a macro-AUCROC of 0.921 for lipid quantification.
  • Demonstrated less than 10% variance compared to traditional chemical analyses for over 82% of food items.
  • Accelerated nutritional screening by 36.9% in student testing.

Conclusions:

  • The developed VLM offers a rapid, high-throughput solution for nutritional analysis.
  • This approach significantly improves the speed and accuracy of nutrient estimation.
  • The study represents a substantial advancement in food science through computational and chemical integration.