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

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

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...

You might also read

Related Articles

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

Sort by

Same author

Comparing ultrasound-derived fat fraction and CT for diagnosing hepatic steatosis: an MRI-PDFF reference study.

Frontiers in medicine·2026

Same author

KIT and FLT3-ITD mutations do not predict outcomes in pediatric core-binding factor acute myeloid leukemia: findings from the C-HUANAN-AML-15 multicenter cohort study.

Annals of hematology·2026

Same author

Effect of delayed cord clamping on maternal and neonatal outcomes during cesarean delivery in twin pregnancies.

BMC pregnancy and childbirth·2026

Same author

Integrated Molecular Risk Stratification and Measurable Residual Disease-Guided Consolidation Improve Outcomes in Pediatric Non-Down Syndrome Acute Megakaryoblastic Leukemia.

Cancer medicine·2026

Same author

Alkali metal N-heteroarenes as a new family of luminescent materials.

Nature communications·2026

Same author

Driving Efficient Tandem Catalysis by Coupling Distinct Active Sites within a Ternary Hydride Lattice.

Journal of the American Chemical Society·2026

Same journal

The Potential for Bioactive Peptide Production in a Fermented Dairy Beverage Based on Chickpea Water Extract Using Proteolytic Lactic Acid Bacteria.

Foods (Basel, Switzerland)·2026

Same journal

Influence of Protein Concentration on Heat-Induced Fouling of Oat Drink.

Foods (Basel, Switzerland)·2026

Same journal

Microalgae as Future Foods: Unlocking Their Potential and Overcoming Barriers to Market Adoption and Commercialization.

Foods (Basel, Switzerland)·2026

Same journal

Effect of High-Intensity Ultrasound and Calcium Chelation on Functional Properties of Casein Micelles.

Foods (Basel, Switzerland)·2026

Same journal

GC-MS and GC-IMS Based Metabolomics Combined with Cellular Assays to Characterize Volatile Compounds and Pharmacological Activity of <i>Lysimachia foenum-graecum</i> Hance from Different Origins.

Foods (Basel, Switzerland)·2026

Same journal

Research on the Potential Mechanism of Guanine Nucleotides Enhancing the Tolerance of <i>Lactiplantibacillus plantarum</i> Y12.

Foods (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 18, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Prediction and Visual Analysis of Food Safety Risk Based on TabNet-GRA.

Yi Chen¹, Hanqiang Li¹, Haifeng Dou¹

¹Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.

Foods (Basel, Switzerland)

|August 26, 2023

Summary

This summary is machine-generated.

This study introduces TabNet-GRA, a new method combining deep learning (TabNet) and grey relational analysis (GRA) for accurate food safety risk prediction. The approach enhances early hazard detection and public health protection.

Keywords:

TabNet early warning food safety grey relational analysis risk prediction visual analysis

More Related Videos

Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ

Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ

Published on: August 9, 2022

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

Related Experiment Videos

Last Updated: Jul 18, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ

Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ

Published on: August 9, 2022

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

Area of Science:

Food Science
Data Science
Public Health

Background:

Effective food safety risk prediction is essential for proactive hazard control.
Existing methods may lack the precision needed for complex food safety data.
Timely detection of foodborne hazards is critical for public health protection.

Purpose of the Study:

To develop and validate a novel food safety risk prediction method, TabNet-GRA.
To integrate deep learning (TabNet) with grey relational analysis (GRA) for enhanced risk assessment.
To create a practical system for food safety risk prediction and visualization.

Main Methods:

Grey Relational Analysis (GRA) was used to calculate comprehensive risk values from fused detection data.
A TabNet deep learning model was trained using detection data and GRA-derived risk values.
Comparative experiments were conducted against six other models to evaluate performance.

Main Results:

The TabNet-based model demonstrated superior fitting ability compared to conventional methods.
A functional food safety risk prediction and visualization system (FSRvis) was successfully implemented.
A case study on cooked meat products validated the method's effectiveness.

Conclusions:

The TabNet-GRA method offers a powerful tool for accurate food safety risk prediction.
The FSRvis system provides valuable visual analysis for targeted risk assessment.
This approach strengthens decision-making for food safety and public health protection.