Chemiresistive Sensor Array and Machine Learning Classification of Food
Author(s)Schroeder, Vera; Evans, Ethan Daniel; Wu, You-Chi Mason; Voll, Constantin-Chri Alexander; McDonald, Benjamin Rebbeck; Savagatrup, Suchol; Swager, Timothy M; ... Show more Show less
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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.
DepartmentMassachusetts Institute of Technology. Department of Chemistry; Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies; Massachusetts Institute of Technology. Department of Biological Engineering
American Chemical Society (ACS)
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
Author's final manuscript