ArticleScientific reports2022
Predicting individual perceptual scent impression from imbalanced dataset using mass spectrum of odorant molecules.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Automatic scent creation by cheminformatics method.Scientific reports · 2024Article
- Clustering and classification for dry bean feature imbalanced data.Scientific reports · 2024Article
- Odor prediction of whiskies based on their molecular composition.Communications chemistry · 2024Article
- A BERT-based pretraining model for extracting molecular structural information from a SMILES sequence.Journal of cheminformatics · 2024Article
- Predicting intensive care unit-acquired weakness: A multilayer perceptron neural network approach.World journal of clinical cases · 2024Article
- Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules.PloS one · 2023Article
- Investigation of the structure-odor relationship using a Transformer model.Journal of cheminformatics · 2022Article
- Extraction of sensing data for desired scent impressions using mass spectra of odorant molecules.Scientific reports · 2022Article
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2 authors.
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Abstract
Predicting odor impression is considered an important step towards measuring the quality of scent in the food, perfume, and cosmetic industries. In odor impression identification and classification, the main target is to predict scent impression while identifying non-target odor impressions are less significant. However, the effectiveness of predictive models depends on the quality of data distribution. Since it is difficult to collect large scale sensory data to create an evenly distributed positive (target odor) and negative (non-target odor) samples, a method is necessary to predict the individual characteristics of scent according to the number of positive samples. Moreover, it is required to predict large number of individual odor impressions from such kind of imbalanced dataset. In this study, we used mass spectrum of flavor molecules and their corresponding odor impressions which have a very disproportioned ratio of positive and negative samples. Thus, we used One-class Classification Support Vector Machine (OCSVM) and Cost-Sensitive MLP (CSMLP) to precisely classify target scent impression. Our experimental results show satisfactory performance in terms of AUC
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