Chemiresistive Sensor Array and Machine Learning Classification of Food

Vera Schroeder, Ethan D. Evans, You Chi Mason Wu, Constantin Christian A. Voll, Benjamin R. McDonald, Suchol Savagatrup, Timothy M. Swager

Research output: Contribution to journalArticlepeer-review

100 Scopus citations


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 languageEnglish (US)
Pages (from-to)2101-2108
Number of pages8
JournalACS Sensors
Issue number8
StatePublished - Aug 23 2019
Externally publishedYes


  • authentication
  • carbon nanotubes
  • chemical sensor
  • electronic nose
  • feature selection
  • nearest neighbors
  • sensor array
  • time series classification

ASJC Scopus subject areas

  • Bioengineering
  • Instrumentation
  • Process Chemistry and Technology
  • Fluid Flow and Transfer Processes


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