A framework for predicting odor threshold values of perfumes by scientific machine learning and transfer learning

Luis M C Oliveira; Vinícius V Santana; Alírio E Rodrigues; Ana M Ribeiro; Idelfonso B R Nogueira

doi:10.1016/j.heliyon.2023.e20813

A framework for predicting odor threshold values of perfumes by scientific machine learning and transfer learning

Heliyon. 2023 Oct 10;9(10):e20813. doi: 10.1016/j.heliyon.2023.e20813. eCollection 2023 Oct.

Authors

Luis M C Oliveira^{1

2}, Vinícius V Santana^{1

2}, Alírio E Rodrigues^{1

2}, Ana M Ribeiro^{1

2}, Idelfonso B R Nogueira³

Affiliations

¹ LSRE-LCM - Laboratory of Separation and Reaction Engineering - Laboratory of Catalysis and Materials, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal.
² ALiCE - Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal.
³ Department of Chemical Engineering, Norwegian University of Science and Technology, Sem Sælandsvei 4, Kjemiblokk 5, Trondheim, Norway.

Abstract

Knowledge of odor thresholds is very important for the perfume industry. Due to the difficulty associated with measuring odor thresholds, empirical models capable of estimating these values can be an invaluable contribution to the field. This work developed a framework based on scientific machine learning strategies. A transfer learning-based strategy was devised, where information from a graph convolutional network predicting semantic odor descriptors was used as input data for the feedforward neural network responsible for estimating odor thresholds for chemical substances based on their molecular structures. The predictive performance of this model was compared to a benchmark odor threshold prediction model based on molecular structures that did not utilize transfer learning. Furthermore, the prediction was compared to a correlation previously proposed in the literature and a dummy regressor. Results demonstrated that the transfer learning-based strategy displayed a better predictive performance, suggesting this technique can be useful for predicting odor thresholds.

Keywords: Artificial neural networks; Feedforward neural networks; Graph convolutional networks; Machine learning; Odor thresholds; Transfer learning.