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

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Predicting Products: Substitution vs. Elimination

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

You might also read

Related Articles

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

Sort by

Same author

Application of plant viruses in biotechnology with relation to artificial intelligence, machine learning, and nanotechnology.

Npj viruses·2026

Same author

Optimising Neurosonography in the NICU: An Audit of 130 Cases and Proposal of a Referral and Reporting Protocol.

Cureus·2026

Same author

PCOSFusion: a hybrid HOG-LBP feature-based approach for PCOS classification using StackPCOS and StackBoostPCOS.

Scientific reports·2026

Same author

BPBiLSTM-IDS: a lightweight intrusion detection framework for cyber-physical UAV networks.

Scientific reports·2026

Same author

A translational IoT healthcare framework integrating secure sensing, energy-aware intelligence, and adaptive emergency response.

Scientific reports·2026

Same author

Hybrid RF FSO communication for 6G improving secrecy and performance under composite weibull lognormal fading and turbulence.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: Jul 14, 2025

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

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

Published on: June 28, 2016

Machine learning-based predictive modelling for the enhancement of wine quality.

Khushboo Jain¹, Keshav Kaushik¹, Sachin Kumar Gupta²

¹School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.

Scientific Reports

|October 9, 2023

Summary

Machine learning models accurately forecast wine quality. Random Forest and Extreme Gradient Boosting achieved high accuracy, with XGBoost reaching 100% using key physiochemical features.

More Related Videos

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

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

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

Related Experiment Videos

Last Updated: Jul 14, 2025

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

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

Published on: June 28, 2016

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

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

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

Area of Science:

Data Science
Machine Learning
Food Science

Background:

Wine quality certification is crucial for the wine industry.
Accurate wine quality prediction aids industry standards and consumer trust.
Physiochemical properties significantly influence wine quality.

Purpose of the Study:

To develop and evaluate machine learning models for wine quality forecasting.
To identify key physiochemical features influencing wine quality.
To optimize predictive models through feature selection and analysis.

Main Methods:

Utilized the red wine dataset (RWD) with eleven physiochemical properties.
Trained and tested five machine learning models, focusing on Random Forest (RF) and Extreme Gradient Boosting (XGBoost).
Employed feature selection techniques, including cluster analysis, to identify essential predictors and address collinearity.

Main Results:

Random Forest and XGBoost demonstrated superior performance among the tested models.
XGBoost achieved 100% accuracy in predicting wine quality when trained and tested with key attributes.
Feature importance analysis identified crucial physiochemical properties for accurate prediction.

Conclusions:

Machine learning models, particularly XGBoost and RF, are effective tools for wine quality forecasting.
Feature selection significantly enhances model accuracy and efficiency.
Identifying and utilizing key physiochemical properties is vital for precise wine quality prediction.