Authors:
Mina Mirshahi
;
Vahid Partovi Nia
and
Luc Adjengue
Affiliation:
Polytechnique Montreal, Canada
Keyword(s):
Artificial Olfaction, Electronic Nose, Gas Sensor, Odor, Outlier, Robust Covariance Estimation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
ICA, PCA, CCA and other Linear Models
;
Missing Data
;
Pattern Recognition
;
Sensors and Early Vision
;
Software Engineering
;
Theory and Methods
Abstract:
An artificial olfaction called electronic nose (e-nose) relies on an array of gas sensors with the capability of mimicking the human sense of smell. Applying an appropriate pattern recognition on the sensor’s output returns odor concentration and odor classification. Odor concentration plays a key role in analyzing odors. Assuring the validity of measurements in each stage of sampling is a critical issue in sampling odors. An accurate prediction for odor concentration demands for careful monitoring of the gas sensor array measurements through time. The existing e-noses capture all odor changes in its environment with possibly varying range of error. Consequently, some measurements may distort the pattern recognition results. We explore e-nose data and provide a statistical algorithm to assess the data validity. Our online algorithm is computationally efficient and treats data as being sampled.