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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2101-2108 |
| Number of pages | 8 |
| Journal | ACS Sensors |
| Volume | 4 |
| Issue number | 8 |
| DOIs | |
| State | Published - Jul 24 2019 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2021-04-13Acknowledged KAUST grant number(s): CRF2015-SENSORS-2719
Acknowledgements: This work was supported by the KAUST sensor project CRF2015-SENSORS-2719 and the Army Research Office through the Institute for Soldier Nanotechnologies and the National Science Foundation (DMR-1410718). S.S. was supported by an F32 Ruth L. Kirschstein National Research Service Award. We want to thank Dr. Nathan A. Romero and Dr. Monika Stolar for their useful suggestions and fruitful conversations.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.