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

Rinaldi Idroes; Teuku Rizky Noviandy; Aga Maulana; Rivansyah Suhendra; Novi Reandy Sasmita; Muslem Muslem; Ghazi Mauer Idroes; Raudhatul Jannah; Razief Perucha Fauzie Afidh; Irvanizam Irvanizam

doi:10.60084/ijds.v1i1.73

Authors

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

DOI:

https://doi.org/10.60084/ijds.v1i1.73

Keywords:

ANFIS, Essential oils, Molecular descriptors

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.

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