Dataset Information

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

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.

SUBMITTER: Oliveira LMC

PROVIDER: S-EPMC10589844 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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

Oliveira Luis M C LMC Santana Vinícius V VV Rodrigues Alírio E AE Ribeiro Ana M AM B R Nogueira Idelfonso I

Heliyon 20231010 10

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 ...[more]

PMID: 37867888

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Dataset Information

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

Publications

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

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