Quantitative structure-property relationship for gas-chromatographic retention indices of volatile organic compounds in colored-quinoa seeds

Rojas, Cristian; Cedillo, Juan Fernando; Sarmiento, Nicole; Pis Diez, Reinaldo; Duchowicz, Pablo R.

doi:10.1016/j.jfca.2024.106843

Journal of Food Composition and Analysis

2025

DOI: 10.1016/j.jfca.2024.106843

|View full text |Cite

Quantitative structure-property relationship for gas-chromatographic retention indices of volatile organic compounds in colored-quinoa seeds

Cristian Rojas,

Juan Fernando Cedillo,

Nicole Sarmiento

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils

Kurniadinur,

Noviandy,

Idroes

et al. 2024

Infolitika J. Data Sci.

View full text Add to dashboard Cite

The Kovats retention index is a critical parameter in gas chromatography used for the identification of volatile compounds in essential oils. Traditional methods for determining the Kovats retention index are often labor-intensive, time-consuming, and prone to inaccuracies due to variations in experimental conditions. This study presents a novel approach combining Artificial Neural Networks (ANN) with Particle Swarm Optimization (PSO) to predict the Kovats retention index of essential oil compounds more accurately and efficiently. The ANN-PSO hybrid model leverages the strengths of both techniques: the ANN's capacity to model complex nonlinear relationships and PSO's capability to optimize hyperparameters by finding the global optimum. The model was trained using a dataset of 340 essential oil compounds with molecular descriptors, with the performance evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results indicate that a simpler ANN configuration with one hidden neuron achieved the lowest RMSE (80.16) and MAPE (5.65%), suggesting that the relationship between the molecular descriptors and the Kovats retention index is not overly complex. This study demonstrates that the ANN-PSO model can serve as an effective tool for predictive modeling of the Kovats retention index, reducing the need for experimental procedures and improving analytical efficiency in essential oil research.

show abstract

Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils

Kurniadinur,

Noviandy,

Idroes

et al. 2024

Infolitika J. Data Sci.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Quantitative structure-property relationship for gas-chromatographic retention indices of volatile organic compounds in colored-quinoa seeds

Cited by 1 publication

References 29 publications

Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils

Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils

Contact Info

Product

Resources

About