Artificial Olfactory System and an Application Thereof

• US 20150000373A1
• Filed: 05/22/2014
• Published: 01/01/2015
• Est. Priority Date: 07/01/2013
• Status: Active Grant

• Current Assignee: Universiti Putra Malaysia (Government of Malaysia)
• Original Assignee: Universiti Putra Malaysia (Government of Malaysia)
• Inventors: Hamidon, Mohd Nizar, Lotfivandvahed, Nasser
• Granted Patent: US 9,651,529 B2
• Time in Patent Office: Days
• US Class Current: 73/23.34
• CPC Class Codes: G01N 33/0001 by organoleptic means
• CPC Class Codes: G01N 33/0031 comprising two or more sensors, e.g. a sensor array electrochemical electrode arrays G01N27/27
• CPC Class Codes: G01N 33/0034 comprising neural networks or related mathematical techniques

Abstract

The present invention relates to an artificial olfactory system (100), comprising of an inlet (101); a gas chamber (110) having a detector means, connected to a data acquisition system (104); a heater (112) and a plurality of fans (115); a humidity absorber (111); an outlet (102); a vacuum pump (103); characterized by the detector means having a plurality of sensors (121) in each of a plurality of clusters (120), wherein the plurality of sensors (121) in each of the plurality of clusters (120) comprises identical sensors capable of responding to a particular gas or vapour. The present invention also relates to a method for detecting a gas or a vapour from the artificial olfactory system (100), comprising the step of exposing the gas or vapour to the plurality of sensors (121) to produce a plurality of output signals from the plurality of sensors (121); transferring the plurality of output signals to the data acquisition system (104); extracting median data from the plurality of output signals; applying a principal component analysis (PCA), neural network, and least square regression analysis on the median data from all of the plurality of clusters (120).

Claims

1. An artificial olfactory system (100), comprising of:

an inlet (101);

a gas chamber (110) having a detector means, connected to a data acquisition system (104);

a heater (112) and a plurality of fans (115) for controlling the temperature in the gas chamber (110);

a humidity absorber (111);

an outlet (102);

a vacuum pump (103) for eliminating remaining polluting gas or vapours;

characterized by;

the detector means having a plurality of sensors (121) in each of a plurality of clusters (120), wherein the plurality of sensors (121) in each of the plurality of clusters (120) comprises identical sensors capable of responding to a particular gas or vapour.

2. An artificial olfactory system (100) according to claim 1, wherein the plurality of sensors (121) comprises a minimum of three sensors in each of the plurality of clusters (120).

3. An artificial olfactory system (100) according to claim 1, wherein the plurality of sensors (121) comprises gas sensors.

4. A method for detecting a gas or a vapour from the artificial olfactory system (100) according to claim 1, comprising the steps of:

exposing the gas or vapour to the plurality of sensors (121) in the gas chamber (110);

thereby producing a plurality of output signals from the plurality of sensors (121) in each of the plurality of clusters (120);

transferring the plurality of output signals to the data acquisition system (104);

extracting median data from the plurality of output signals from each of the plurality of clusters (120) by applying statistic median method;

applying a principal component analysis (PCA) on the median data from all of the plurality of clusters (120) for gas or vapour identification and feature reduction;

applying a neural network on the median data from all of the plurality of clusters (120) for evaluating the classification of the gas or vapour;

applying a least square regression analysis on the median data from all of the plurality of clusters (120) to estimate the concentration of the gas or vapour.

5. A method for detecting a gas or a vapour from the artificial olfactory system (100) according to claim 4, wherein the neural network is a multi-layer perceptron (MLP) classifier.