Abstract
Machine learning and data analytics are being increasingly used for quantitative structure property relation (QSPR) applications in the chemical domain where the traditional Edisonian approach towards knowledge-discovery have not been fruitful. The perception of odorant stimuli is one such application as olfaction is the least understood among all the other senses. In this study, we employ machine learning based algorithms and data analytics to address the efficacy of using a data-driven approach to predict the perceptual attributes of an odorant namely the odorant characters (OC) of “sweet” and “musky”. We first analyze a psychophysical dataset containing perceptual ratings of 55 subjects to reveal patterns in the ratings given by subjects. We then use the data to train several machine learning algorithms such as random forest, gradient boosting and support vector machine for prediction of the odor characters and report the structural features correlating well with the odor characters based on the optimal model. Furthermore, we analyze the impact of the data quality on the performance of the models by comparing the semantic descriptors generally associated with a given odorant to its perception by majority of the subjects. The study presents a methodology for developing models for odor perception and provides insights on the perception of odorants by untrained human subjects and the effect of the inherent bias in the perception data on the model performance. The models and methodology developed here could be used for predicting odor characters of new odorants.
Highlights
Machine learning and data analytics are being increasingly used for quantitative structure property relation (QSPR) applications in the chemical domain where the traditional Edisonian approach towards knowledge-discovery have not been fruitful
In this study, the psychophysical dataset developed by Keller et al.[38] was utilized to develop machine learning-based classification models for prediction of odor characters
The visualization of the dataset revealed groups of compounds that could be explored to make new fragrance formulations. Simple data analytics such as these show that the presence or absence of a functional group in a molecule can be related to the hedonic attributes of the molecule
Summary
Machine learning and data analytics are being increasingly used for quantitative structure property relation (QSPR) applications in the chemical domain where the traditional Edisonian approach towards knowledge-discovery have not been fruitful. We analyze the impact of the data quality on the performance of the models by comparing the semantic descriptors generally associated with a given odorant to its perception by majority of the subjects. Researchers collect odor perception data through a variety of approaches such as verbal profiling, similarity ratings and sorting[9,10]. Subjects are asked to rate the odors against a set of predefined semantic descriptors that they would associate with the odors[11,12] Such an approach requires an individual to make a comparison between an actual sensation and an abstract sensation based on their interpretation of the semantic descriptors. Indole has a floral smell at low concentrations while it smells putrid at higher concentrations[30]
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