23-01-2012, 03:21 PM
ELECTRONIC NOSE
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Introduction
An electronic nose system typically consists of a multisensor array, an information-processing unit such as an artificial neural network (ANN), software with digital pattern-recognition algorithms, and reference-library databases . The cross-reactive sensor array is composed of incrementally-different sensors chosen to respond to a wide range of chemical classes and discriminate diverse mixtures of possible analytes. The output from individual sensors are collectively assembled and integrated to produce a distinct digital response pattern. Identification and classification of an analyte mixture is accomplished through recognition of this unique aroma signature (electronic fingerprint) of collective sensor responses
The Role of Aroma in Human Society and Commerce
The sense of smell has long played a fundamental role in human development and biosocial interactions. Consequently, the olfactory sense has become a key element in the development of many commercial industries that manipulate the aroma properties of their manufactured goods in order to improve product appeal, quality, and consistency so that consumers quickly identify with individual brands having unique scents.
Electronic Nose Instrumentation
Gardner and Bartlett provided a basic requisite definition of an electronic-nose device with a list of necessary components (as follows):
1. an aroma delivery system, which transfers the volatile aromatic molecules from the source material to the sensor array system
2. a chamber where sensors are housed: this has usually fixed temperature and humidity, which otherwise would affect the aroma molecules adsorption.
3. an electronic transistor which converts the chemical signal into an electrical signal, amplifies and conditions it.
Data Analysis for Electronic Noses
The digital outputs generated by e-nose sensors have to be analyzed and interpreted in order to provide useful information to the operator. Commercially available analysis techniques fall into three main categories as follows :
1. Graphical analyses: bar chart, profile, polar and offset polar plots
2. Multivariate data analyses (MDA): principal component analysis (PCA), canonical discriminate analysis (CDA), featured within (FW) and cluster analysis (CA)
3. Network analyses: artificial neural network (ANN) and radial basis function (RBF)
4. a digital converter that converts the signal from electrical (analog) to digital
