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Sensor-Array Optimization Based on Time-Series Data Analytics for Sanitation-Related Malodor Detection.

Publication ,  Journal Article
Zhou, J; Welling, CM; Vasquez, MM; Grego, S; Chakrabarty, K
Published in: IEEE transactions on biomedical circuits and systems
August 2020

There is an unmet need for a low-cost instrumented technology for detecting sanitation-related malodor as an alert for maintenance around shared toilets and emerging technologies for onsite waste treatment. In this article, our approach to an electronic nose for sanitation-related malodor is based on the use of electrochemical gas sensors, and machine-learning techniques for sensor selection and odor classification. We screened 10 sensors from different vendors with specific target gases and recorded their response to malodor from fecal specimens and urine specimens, and confounding good odors such as popcorn. The analysis of 180 odor exposures data by two feature-selection methods based on mutual information indicates that, for malodor detection, the decision tree (DT) classifier with seven features from four sensors provides 88.0% balanced accuracy and 85.1% macro F1 score. However, the k-nearest-neighbors (KNN) classifier with only three features (from two sensors) obtains 83.3% balanced accuracy and 81.3% macro F1 score. For classification of urine against feces malodor, a balanced accuracy of 94.0% and a macro F1 score of 92.9% are achieved using only four features from three sensors and a logistic regression (LR) classifier.

Duke Scholars

Published In

IEEE transactions on biomedical circuits and systems

DOI

EISSN

1940-9990

ISSN

1932-4545

Publication Date

August 2020

Volume

14

Issue

4

Start / End Page

705 / 714

Related Subject Headings

  • Urine
  • Toilet Facilities
  • Odorants
  • Neural Networks, Computer
  • Machine Learning
  • Humans
  • Gases
  • Feces
  • Equipment Design
  • Electronic Nose
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zhou, J., Welling, C. M., Vasquez, M. M., Grego, S., & Chakrabarty, K. (2020). Sensor-Array Optimization Based on Time-Series Data Analytics for Sanitation-Related Malodor Detection. IEEE Transactions on Biomedical Circuits and Systems, 14(4), 705–714. https://doi.org/10.1109/tbcas.2020.3002180
Zhou, Jin, Claire M. Welling, Mariana M. Vasquez, Sonia Grego, and Krishnendu Chakrabarty. “Sensor-Array Optimization Based on Time-Series Data Analytics for Sanitation-Related Malodor Detection.IEEE Transactions on Biomedical Circuits and Systems 14, no. 4 (August 2020): 705–14. https://doi.org/10.1109/tbcas.2020.3002180.
Zhou J, Welling CM, Vasquez MM, Grego S, Chakrabarty K. Sensor-Array Optimization Based on Time-Series Data Analytics for Sanitation-Related Malodor Detection. IEEE transactions on biomedical circuits and systems. 2020 Aug;14(4):705–14.
Zhou, Jin, et al. “Sensor-Array Optimization Based on Time-Series Data Analytics for Sanitation-Related Malodor Detection.IEEE Transactions on Biomedical Circuits and Systems, vol. 14, no. 4, Aug. 2020, pp. 705–14. Epmc, doi:10.1109/tbcas.2020.3002180.
Zhou J, Welling CM, Vasquez MM, Grego S, Chakrabarty K. Sensor-Array Optimization Based on Time-Series Data Analytics for Sanitation-Related Malodor Detection. IEEE transactions on biomedical circuits and systems. 2020 Aug;14(4):705–714.

Published In

IEEE transactions on biomedical circuits and systems

DOI

EISSN

1940-9990

ISSN

1932-4545

Publication Date

August 2020

Volume

14

Issue

4

Start / End Page

705 / 714

Related Subject Headings

  • Urine
  • Toilet Facilities
  • Odorants
  • Neural Networks, Computer
  • Machine Learning
  • Humans
  • Gases
  • Feces
  • Equipment Design
  • Electronic Nose