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[[Vol. 38]Quantifying Smell with Nanoparticles-functionalized Membrane-type Surface Stress Sensor and тАЬData-drivenтАЭ Analysis_ WPI-MANA.pdf](https://mdr.nims.go.jp/filesets/af0c0e9b-4976-427a-b097-0568b9c85545/download)

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International Center for Materials Nanoarchitectonics (WPI-MANA)

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[[Research Highlights Vol.38] Quantifying Smell with Nanoparticles-functionalized Membrane-type Surface Stress Sensor and “Data-driven” Analysis](https://mdr.nims.go.jp/datasets/df50921f-298b-4ea0-b8c6-233cad7fe884)

## Fulltext

2022/04/04 10:06 Quantifying Smell with Nanoparticles-functionalized Membrane-type Surface Stress Sensor and “Data-driven” Analysis| MANAhttps://www.nims.go.jp/mana/research/highlights/vol38.html 1/2Previous  Index  NextResearch Highlights[Vol. 38]Quantifying Smell with Nanoparticles-functionalized Membrane-type SurfaceStress Sensor and “Data-driven” Analysis29 Jun, 2018Figure: An illustration of the present quantification of alcohol content by the data-drivennanomechanical sensing approach.Chromatography is widely used for the quantitative identification of specific components inmixtures of compounds. For example, quantifying smell requires analysis of a complex mixturecomposed of thousands of chemical compounds. Although chromatography systems can be used todo so, the equipment is usually bulky and measurement procedures necessitate the time-consuming process to separate all the components into single species.Here, Shiba, Tamura, Imamura and Yoshikawa at the International Center for MaterialsNanoarchitectonics (MANA), Tsukuba, Japan, report on combining their invention, the Membrane-type Surface stress Sensor (MSS), functionalized nanoparticles, and “data-driven” analysis toquantitatively determine the concentration of alcohol from smell data contained in liquors withvarying alcohol content.Specifically, the surface of the MSS array was covered with four types of silica/titania functionalnanoparticles that adsorb different target molecules in the test samples, and patterns of electricalsignals were recorded.https://www.nims.go.jp/mana/research/highlights/vol37.htmlhttps://www.nims.go.jp/mana/research/highlights/index.htmlhttps://www.nims.go.jp/mana/research/highlights/vol39.html2022/04/04 10:06 Quantifying Smell with Nanoparticles-functionalized Membrane-type Surface Stress Sensor and “Data-driven” Analysis| MANAhttps://www.nims.go.jp/mana/research/highlights/vol38.html 2/2Next, machine learning was used to analyze the massive amounts of data obtained from theelectrical signal patterns to establish a prediction model for quantifying alcohol concentration of thesmell sample.Notably, materials more suitable for alcohol smells were selected based on machine learningresults, and thereby the accuracy of prediction was improved.“The importance of this research is not the fact that we determined the alcohol concentration, butthat this approach enables the quantification of many other arbitrary indices,” says Shiba. “Thesefindings have paved the way for applications for quantitative evaluation of natural products, whichwe have demonstrated in collaboration with universities and industry.”Reference“Data-driven nanomechanical sensing: specific information extraction from a complex system”Kota Shiba, Ryo Tamura, Gaku Imamura, and Genki YoshikawaJournal : Scientific Reports, 7: 3661 (2017).DOI : 10.1038/s41598-017-03875-7AffiliationsInternational Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for MaterialsScience (NIMS), Namiki 1-1, Tsukuba, Ibaraki 305-0044, JapanContact informationInternational Center for Materials Nanoarchitectonics(WPI-MANA)National Institute for Materials Science1-1 Namiki, Tsukuba, Ibaraki 305-0044 JapanPhone: +81-29-860-4710E-mail: mana-pr[AT]ml.nims.go.jphttps://samurai.nims.go.jp/profiles/tamura_ryo?locale=enhttps://samurai.nims.go.jp/profiles/imamura_gaku?locale=enhttps://www.nature.com/articles/s41598-017-03875-7