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Functional Nanoparticles-Coated Nanomechanical Sensor Arrays for Machine Learning-Based Quantitative Odor Analysis
journal contribution
posted on 2018-08-15, 20:14 authored by Kota Shiba, Ryo Tamura, Takako Sugiyama, Yuko Kameyama, Keiko Koda, Eri Sakon, Kosuke Minami, Huynh Thien Ngo, Gaku Imamura, Koji Tsuda, Genki YoshikawaA sensing
signal obtained by measuring an odor usually contains
varied information that reflects an origin of the odor itself, while
an effective approach is required to reasonably analyze informative
data to derive the desired information. Herein, we demonstrate that
quantitative odor analysis was achieved through systematic material
design-based nanomechanical sensing combined with machine learning.
A ternary mixture consisting of water, ethanol, and methanol was selected
as a model system where a target molecule coexists with structurally
similar species in a humidified condition. To predict the concentration
of each species in the system via the data-driven approach, six types
of nanoparticles functionalized with hydroxyl, aminopropyl, phenyl,
and/or octadecyl groups were synthesized as a receptor coating of
a nanomechanical sensor. Then, a machine learning model based on Gaussian
process regression was trained with sensing data sets obtained from
the samples with diverse concentrations. As a result, the octadecyl-modified
nanoparticles enhanced prediction accuracy for water while the use
of both octadecyl and aminopropyl groups was indicated to be a key
for a better prediction accuracy for ethanol and methanol. As the
prediction accuracy for ethanol and methanol was improved by introducing
two additional nanoparticles with finely controlled octadecyl and
aminopropyl amount, the feedback obtained by the present machine learning
was effectively utilized to optimize material design for better performance.
We demonstrate through this study that various information which was
extracted from plenty of experimental data sets was successfully combined
with our knowledge to produce wisdom for addressing a critical issue
in gas phase sensing.
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target molecule coexistsdata setsnanoparticleapproachprediction accuracyodorethanolMachine Learning-Based Quantitative Odor AnalysisaminopropylGaussian process regressionFunctional Nanoparticles-Coated Nanomechanical Sensor Arraysconcentrationspeciesinformationmethanolmaterial design-based nanomechanicalmodeloctadecyl
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