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

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

On-capillary derivatization using a hybrid artificial neural network-genetic algorithm approach.

Toni Riveros¹, Grady Hanrahan, Sarah Muliadi

¹Department of Chemistry and Biochemistry, California State University, Los Angeles, 5151 State University Drive, Los Angeles, CA 90032, USA.

|September 22, 2009

Summary

This study introduces a novel hybrid artificial neural network-genetic algorithm (ANN-GA) for optimizing dipeptide derivatization. The ANN-GA approach demonstrated superior performance in maximizing dipeptide conversion compared to traditional methods.

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

A Man with Neutropenic Fever, Perianal Pain, and Jaundice.

Annals of the American Thoracic Society·2024

Same author

Respiratory Mechanics in a Cohort of Critically Ill Subjects With COVID-19 Infection.

Respiratory care·2021

Same author

Emergency department characteristics and associations with intensive care admission among patients with coronavirus disease 2019.

Journal of the American College of Emergency Physicians open·2021

Same author

Thread- and Capillary Tube-Based Electrodes for the Detection of Glucose and Acetylthiocholine.

Micromachines·2020

Same author

A primer on proning in the emergency department.

Journal of the American College of Emergency Physicians open·2020

Same author

Bedside Optic Nerve Ultrasonography for Diagnosing Increased Intracranial Pressure.

Annals of internal medicine·2020

Area of Science:

Analytical Chemistry
Computational Chemistry
Biochemistry

Background:

Dipeptide derivatization is crucial for analytical methods.
Optimization of reaction conditions is essential for efficient derivatization.
Existing methods may lack predictive power for complex reaction systems.

Purpose of the Study:

To develop and validate a hybrid artificial neural network-genetic algorithm (ANN-GA) for optimizing on-capillary dipeptide derivatization.
To determine optimal parameters for the derivatization of D-Ala-D-Ala using phthalic anhydride.
To compare the performance of the hybrid ANN-GA model against a standalone ANN model.

Main Methods:

A hybrid approach combining artificial neural networks (ANN) with a genetic algorithm (GA) was developed.

Related Experiment Videos

Last Updated: Jun 20, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

GA was employed to optimize ANN structure and identify key input parameters: phthalic anhydride injection volume, injection time, and voltage.

The model was trained and validated using experimental data for D-Ala-D-Ala derivatization.

Main Results:

The hybrid ANN-GA model successfully optimized the derivatization process, achieving maximum conversion of D-Ala-D-Ala.
GA-driven optimization identified effective network parameters, leading to improved predictive ability.
The hybrid approach outperformed a standard ANN model in terms of training data utilization and predictive accuracy.

Conclusions:

The hybrid ANN-GA approach offers a powerful and efficient method for optimizing on-capillary dipeptide derivatization.
This methodology provides superior predictive capabilities compared to non-optimized ANN models.
The developed model has potential applications in analyzing other organic-based reaction systems.