A fragrance prediction model for molecules using rough set-based machine learning

Authors

• Shie Teck Tiew, University of Nottingham
• Yick Eu Chew, University of Nottingham
• Jia Wen Chong, University of Nottingham
• Raymond Girard R. Tan, De La Salle University, Manila
• Kathleen B. Aviso, De La Salle University, Manila
• Nishanth G. Chemmangattuvalappil, University of Nottingham

College

Gokongwei College of Engineering

Department/Unit

Chemical Engineering

Document Type

Article

Source Title

Chemie Ingenieur Technik

Volume

95

Issue

3

First Page

438

Last Page

446

Publication Date

11-2022

Abstract

In this work, a novel machine learning based methodology was developed to predict fragrance from the molecular structure and the effect of the subjects attributes on odour perception. As fragrance is linked to the molecular structure and interactions, topological indices are used to develop a predictive model. Rough set-based machine learning is used to generate rule-based models that link the topology of fragrant molecules and dilution to their respective odour characteristics. The results show that the generated models are effective in determining the odour characteristic of molecules.

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Recommended Citation

Tiew, S., Chew, Y., Chong, J., Tan, R. R., Aviso, K. B., & Chemmangattuvalappil, N. G. (2022). A fragrance prediction model for molecules using rough set-based machine learning. Chemie Ingenieur Technik, 95 (3), 438-446. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/15368

Disciplines

Chemical Engineering

Keywords

Odors; Machine learning; Olfactory sensors

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