Method and apparatus for estimating odor concentration using an electronic nose

The present invention relates to a method and apparatus for obtaining useful measurements from an electronic nose and, more particularly, to a method and system for obtaining an estimate of odor concentration from data obtained from a sensor-array type electronic nose. A system and method for obtain...

Full description

Saved in:
Bibliographic Details
Main Authors Guoliang, Qu, Feddes, John R, Coleman, Richard N, Armstrong, William W, Leonard, Jerry J
Format Patent
LanguageEnglish
Published 25.06.2002
Online AccessGet full text

Cover

More Information
Summary:The present invention relates to a method and apparatus for obtaining useful measurements from an electronic nose and, more particularly, to a method and system for obtaining an estimate of odor concentration from data obtained from a sensor-array type electronic nose. A system and method for obtaining an estimate of the concentration of an odor in an air sample from data obtained by evaluating the sample with a sensor-array type electronic nose. Principal components analysis is applied to a set of air sample data including sensor-array data obtained from evaluating the air sample with the sensor-array type electronic nose and measurements of the humidity of the air sample and clean reference air used by the electronic nose to obtain a predetermined number of principal components of the air sample data. The principal components obtained from the air sample are used as inputs to a neural network to obtain as output an estimate of the concentration of the odor in the air sample. The neural network uses parameters obtained by using an olfactometer to obtain discrete measurements of odor concentration from each of a plurality of calibration samples of air containing the odor, using the sensor-array type electronic nose to obtain a discrete set of calibration data from each of the calibration samples, each set including sensor-array data and measurements of the humidity of the calibration sample and the clean reference air used by the electronic nose, applying principal components analysis to each set of calibration data to obtain a discrete set of the predetermined number of principal components, and training a neural network using the sets of principal components as input data and the corresponding measured odor concentrations as expected output to obtain the parameters of a trained neural network.