Extraction of sensing data for desired scent impressions using mass spectra of odorant molecules

Debnath, Tanoy; Nakamoto, Takamichi

doi:10.1038/s41598-022-20388-0

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…In other words, it is imperative to reduce the number of odor molecules used for the odor reconstitution of human olfactory DX. There is an attempt to reduce the number of odor molecules by GC-MS analysis of essential oils to sort out odor molecules that exhibit the same odor quality and reconstitute the odor quality of any given essential oil [31], but this method is applicable only to specific categories of odors (e.g., essential oils) and cannot be used to reconstitute a wide range of all odors.…”

Section: Preliminary Odor Reconstitutionmentioning

confidence: 99%

Human Olfactory Receptor Sensor for Odor Reconstitution

Kuroda,

Nakaya-Kishi,

Tatematsu

et al. 2023

Sensors

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Among the five human senses, light, sound, and force perceived by the eye, ear, and skin, respectively are physical phenomena, and therefore can be easily measured and expressed as objective, univocal, and simple digital data with physical quantity. However, as taste and odor molecules perceived by the tongue and nose are chemical phenomena, it has been difficult to express them as objective and univocal digital data, since no reference chemicals can be defined. Therefore, while the recording, saving, transmitting to remote locations, and replaying of human visual, auditory, and tactile information as digital data in digital devices have been realized (this series of data flow is defined as DX (digital transformation) in this review), the DX of human taste and odor information is not yet in the realization stage. Particularly, since there are at least 400,000 types of odor molecules and an infinite number of complex odors that are mixtures of these molecules, it has been considered extremely difficult to realize “human olfactory DX” by converting all odors perceived by human olfaction into digital data. In this review, we discuss the current status and future prospects of the development of “human olfactory DX”, which we believe can be realized by utilizing odor sensors that employ the olfactory receptors (ORs) that support human olfaction as sensing molecules (i.e., human OR sensor).

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Section: Preliminary Odor Reconstitutionmentioning

confidence: 99%

Human Olfactory Receptor Sensor for Odor Reconstitution

Kuroda,

Nakaya-Kishi,

Tatematsu

et al. 2023

Sensors

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“…In parallel, other methods predict the properties of compounds directly from the mass spectra without the intermediate step of structure determination. Some examples include definition of a class of chemical compounds, CANOPUS tool (average precision = 99.7%), its toxicity ( R 2 = 0.7 for regression model and balanced accuracy (BA) = 0.7 for classification model), or its odor . However, to the best of our knowledge, there have been no attempts to predict the taste of a compound from its mass spectrum.…”

Section: Introductionmentioning

confidence: 99%

BitterMasS: Predicting Bitterness from Mass Spectra

Ziaikin,

Tello,

Peterson

et al. 2024

J. Agric. Food Chem.

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Bitter compounds are common in nature and among drugs. Previously, machine learning tools were developed to predict bitterness from the chemical structure. However, known structures are estimated to represent only 5−10% of the metabolome, and the rest remain unassigned or "dark". We present BitterMasS, a Random Forest classifier that was trained on 5414 experimental mass spectra of bitter and nonbitter compounds, achieving precision = 0.83 and recall = 0.90 for an internal test set. Next, the model was tested against spectra newly extracted from the literature 106 bitter and nonbitter compounds and for additional spectra measured for 26 compounds. For these external test cases, BitterMasS exhibited 67% precision and 93% recall for the first and 58% accuracy and 99% recall for the second. The spectrum−bitterness prediction strategy was more effective than the spectrum−structure−bitterness prediction strategy and covered more compounds. These encouraging results suggest that BitterMasS can be used to predict bitter compounds in the metabolome without the need for structural assignment of individual molecules. This may enable identification of bitter compounds from metabolomics analyses, for comparing potential bitterness levels obtained by different treatments of samples and for monitoring bitterness changes overtime.

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