Odor Impression Prediction from Mass Spectra

Nozaki, Yuji; Nakamoto, Takamichi

doi:10.1371/journal.pone.0157030

Cited by 33 publications

(22 citation statements)

References 22 publications

(18 reference statements)

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“…But even though the implication is that odor perception should be highly subjective, studies have shown that genetic variability in odorant receptors (ORs) contributes to odor perception. Equally, machine learning has accurately predicted perceptual descriptors of odorants from chemical features, suggesting that physicochemical properties influence perception ( Debnath et al., 2019 ; Gutiérrez et al., 2018 ; Keller et al., 2017 ; Khan et al., 2007 ; Licon et al., 2019 ; Nozaki and Nakamoto, 2016 ; Sanchez-Lengeling et al., 2019 ). Moreover, modeling human odor perception using a large semantic similarity space has shown that accurate predictions of perceptual ratings are possible even when training and prediction are done on completely different study samples.…”

Section: Introductionmentioning

confidence: 99%

Predicting Human Olfactory Perception from Activities of Odorant Receptors

Kowalewski

Ray

2020

iScience

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Section: Introductionmentioning

confidence: 99%

Predicting Human Olfactory Perception from Activities of Odorant Receptors

Kowalewski

Ray

2020

iScience

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“…We previously proposed a predictive model of odor impression using a nine-layer neural network [ 7 ]. In the predictive model, which uses the results of Dravnieks’ evaluation test, 146 different odor characteristics were predicted with a correlation coefficient of 0.76 on a validation set.…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling for odor character of a chemical using machine learning combined with natural language processing

Nozaki¹,

Nakamoto²

2018

PLoS ONE

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Recent studies on machine learning technology have reported successful performances in some visual and auditory recognition tasks, while little has been reported in the field of olfaction. In this paper we report computational methods to predict the odor impression of a chemical from its physicochemical properties. Our predictive model utilizes nonlinear dimensionality reduction on mass spectra data and performs the clustering of descriptors by natural language processing. Sensory evaluation is widely used to measure human impressions to smell or taste by using verbal descriptors, such as “spicy” and “sweet”. However, as it requires significant amounts of time and human resources, a large-scale sensory evaluation test is difficult to perform. Our model successfully predicts a group of descriptors for a target chemical through a series of computer simulations. Although the training text data used in the language modeling is not specialized for olfaction, the experimental results show that our method is useful for analyzing sensory datasets. This is the first report to combine machine olfaction with natural language processing for odor character prediction.

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“…nonlinear high dimensional space of mass spectra [9] into its bottleneck latent space. Then, these reduced feature vectors of mass spectra are used as an input to neural network to predict the odor descriptor groups.…”

Section: Plos Onementioning

confidence: 99%

Predicting human odor perception represented by continuous values from mass spectra of essential oils resembling chemical mixtures

Debnath

Nakamoto

2020

PLoS ONE

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There have been recent advances in predicting odor characteristics using molecular structure parameters of chemicals. Although the molecular structure parameters are available for each chemical, they cannot be used for chemical mixtures. This study will elucidate a computational method of predicting human odor perception from the mass spectra of chemical mixtures such as essential oils. Furthermore, a method for obtaining similarity among odor descriptors has been proposed although the dataset contains binary values only. When the database indicates a set of odor descriptors for one sample, only binary data are available and the correlation between the similar descriptors disappears. Thus, the prediction performance degrades for not considering the similarity among the odor descriptors. Since mass spectra dataset is highly dimensional, we use auto-encoder to learn the compressed representation from the mass spectra of essential oils in its bottleneck hidden layer and then accomplishes the hierarchical clustering to create odor descriptor groups with similar odor impressions using a matrix of continuous value-based correlation coefficient as well as natural language processing. This work will help to expatiate the process of overcoming binary value problem and find out the similarity among odor descriptors using machine learning with natural language semantic representation of words. To overcome the problem of disproportionate ratio of positive and negative class for both the continuous value-based correlation coefficient and word similarity based models, we use Synthetic Minority Oversampling Technique (SMOTE). This model allows us to predict human odor perception through computer simulations by forming odor descriptors group. Accordingly, this study demonstrates the feasibility of ensembling machine learning with natural language processing and SMOTE approach for predicting odor descriptor group from mass spectra of essential oils.

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Odor Impression Prediction from Mass Spectra

Cited by 33 publications

References 22 publications

Predicting Human Olfactory Perception from Activities of Odorant Receptors

Predicting Human Olfactory Perception from Activities of Odorant Receptors

Predictive modeling for odor character of a chemical using machine learning combined with natural language processing

Predicting human odor perception represented by continuous values from mass spectra of essential oils resembling chemical mixtures

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