Chemiresistive Sensor Array and Machine Learning Classification of Food
Name
doi-10.1021 acssensors.9b00825.pdf
Description
Accepted version
Size
732.62 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Schroeder, Vera
Evans, Ethan Daniel
Wu, You-Chi Mason
Voll, Constantin-Chri Alexander
McDonald, Benjamin Rebbeck
Savagatrup, Suchol
Swager, Timothy M
Date Issued
July 2019
Journal
ACS Sensors
Publisher
American Chemical Society (ACS)
Citation
Schroeder, Vera et al. "Chemiresistive Sensor Array and Machine Learning Classification of Food." ACS Sensors 4, 8 (July 2019): 2101–2108 © 2019 American Chemical Society
Version
Author's final manuscript
Abstract
Successful identification of complex odors by sensor arrays remains a challenging problem. Herein, we report robust, category-specific multiclass-time series classification using an array of 20 carbon nanotube-based chemical sensors. We differentiate between samples of cheese, liquor, and edible oil based on their odor. In a two-stage machine-learning approach, we first obtain an optimal subset of sensors specific to each category and then validate this subset using an independent and expanded data set. We determined the optimal selectors via independent selector classification accuracy, as well as a combinatorial scan of all 4845 possible four selector combinations. We performed sample classification using two models - a k-nearest neighbors model and a random forest model trained on extracted features. This protocol led to high classification accuracy in the independent test sets for five cheese and five liquor samples (accuracies of 91% and 78%, respectively) and only a slightly lower (73%) accuracy on a five edible oil data set.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
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DOI of Published Version
https://doi.org/10.1021/acssensors.9b00825
