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

Effect of Bean-Derived Soluble Dietary Fibers on Macrophage Function In Vitro.

Foods (Basel, Switzerland)·2026
Same author

Optimized Alkaline Hydrolysis for Recovering Ferulated Arabinoxylan Biopolymers from Maize Bran with Antioxidant Functionality.

Polymers·2026
Same author

Beyond α-Glucosidase and α-Amylase Inhibition: Integrated In Vitro and Multi-Scale In Silico Insights into the Antidiabetic and Antioxidant Mechanisms of <i>Oxalis corniculata</i> L. Aerial Parts.

Molecules (Basel, Switzerland)·2026
Same author

In Search of the Most Significant Potential G-Quadruplexes in SARS-CoV-2 RNA: Genomic Analysis.

Viruses·2026
Same author

Toward an Emerging Public Health Paradigm: Agriculture and Food Production for Health.

Foods (Basel, Switzerland)·2026
Same author

Hemp seed mitigates colonic inflammation through macrophage polarization and microbiota-barrier axis restoration.

Food & function·2025

Related Experiment Video

Updated: Jun 21, 2025

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes
10:30

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes

Published on: June 10, 2022

6.8K

Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR

Kweeni Iduoku1,2, Marvellous Ngongang1, Jayani Kulathunga3,4

  • 1Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA.

Foods (Basel, Switzerland)
|July 13, 2024
PubMed
Summary

Beta-cyclodextrin effectively masks bitterness in phenolic compounds by encapsulating them. Machine learning models accurately predict binding affinity, identifying key molecular features for enhanced bitterness masking in food and pharmaceuticals.

Keywords:
QSARbinding affinityflavorsmachine learningβ-cyclodextrin

More Related Videos

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
09:12

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

623
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.6K

Related Experiment Videos

Last Updated: Jun 21, 2025

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes
10:30

A Generalized Method for Determining Free Soluble Phenolic Acid Composition and Antioxidant Capacity of Cereals and Legumes

Published on: June 10, 2022

6.8K
Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
09:12

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

623
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.6K

Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Cyclodextrins, particularly beta-cyclodextrin, are widely used for encapsulating hydro-sensitive molecules across various industries.
  • Beta-cyclodextrin is crucial for masking the bitterness of phenolic compounds, such as those found in wheat bran.
  • A need exists for predictive models to assess beta-cyclodextrin's bitterness masking efficacy for diverse phenolic compounds.

Purpose of the Study:

  • To develop robust predictive models for assessing beta-cyclodextrin's bitterness masking capabilities.
  • To investigate the binding interactions between beta-cyclodextrin and phenolic acids.
  • To identify key molecular descriptors influencing the binding affinity.

Main Methods:

  • Utilized a dataset of 20 phenolic acids docked into the beta-cyclodextrin cavity to determine binding constants.
  • Integrated docking data with topological, topographical, and quantum-chemical features.
  • Employed machine learning, specifically a combination of genetic algorithm (GA) and multiple linear regression (MLR), to build Quantitative Structure-Activity Relationship (QSAR) models.

Main Results:

  • Developed three distinct ML/QSAR models for predicting binding constants with high accuracy.
  • Achieved excellent performance metrics, including correlation coefficients of 0.969 for training and 0.984 for test sets.
  • Identified molecular features positively contributing to binding affinity, such as six-membered rings, branching, electronegativity, and polar surface area.

Conclusions:

  • The developed ML/QSAR models provide a reliable tool for predicting beta-cyclodextrin binding affinity with phenolic compounds.
  • Understanding the key molecular features influencing binding can guide the selection of compounds for effective bitterness masking.
  • This study enhances the application of cyclodextrins in pharmaceuticals, food science, and agriculture by providing predictive insights.