Infolitika Journal of Data Science
Vol. 1 No. 1 (2023): September 2023

ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography

Rinaldi Idroes (Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Department of Pharmacy, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia)

Teuku Rizky Noviandy (Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia)
Aga Maulana (Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia)
Rivansyah Suhendra (Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh)
Novi Reandy Sasmita (Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia)
Muslem Muslem (Department of Chemistry, Faculty of Science and Technology, Universitas Islam Negeri Ar-Raniry, Banda Aceh 23111, Indonesia)
Ghazi Mauer Idroes (Department of Occupational Health and Safety, Faculty of Health Sciences, Universitas Abulyatama, Aceh Besar 23372, Indonesia)
Raudhatul Jannah (Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia)
Razief Perucha Fauzie Afidh (Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia)
Irvanizam Irvanizam (Department of Informatics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia)



Article Info

Publish Date
07 Sep 2023

Abstract

This study aims to evaluate the implementation and effectiveness of the Adaptive Neuro-Fuzzy Inference System (ANFIS) based Quantitative Structure Retention Relationship (QSRR) to predict the Kovats retention index of compounds in gas chromatography. The model was trained using 340 essential oil compounds and their molecular descriptors. The evaluation of the ANFIS models revealed promising results, achieving an R2 of 0.974, an RMSE of 48.12, and an MAPE of 3.3% on the testing set. These findings highlight the ANFIS approach as remarkably accurate in its predictive capacity for determining the Kovats retention index in the context of gas chromatography. This study provides valuable perspectives on the efficiency of retention index prediction through ANFIS-based QSRR methods and the potential practicality in compound analysis and chromatographic optimization.

Copyrights © 2023






Journal Info

Abbrev

ijds

Publisher

Subject

Computer Science & IT Decision Sciences, Operations Research & Management Engineering

Description

Infolitika Journal of Data Science is a distinguished international scientific journal that showcases high caliber original research articles and comprehensive review papers in the field of data science. The journals core mission is to stimulate interdisciplinary research collaboration, facilitate ...