Preprocessing of matrix QCM sensors data for the classification by means of neural network

An experimental comparison of linear and non-linear pre-processing methods for olfactory data is made. The original data are formed by 280 values of reaction from six quartz crystal microbalance (QCM) sensors taken at 1 s intervals. Data vectors are processed with the non-linear maximum filter or by...

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Bibliographic Details
Published inSensors and actuators. B, Chemical Vol. 106; no. 1; pp. 158 - 163
Main Authors Reznik, A.M., Galinskaya, A.A., Dekhtyarenko, O.K., Nowicki, D.W.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 29.04.2005
Subjects
Data preprocessing
Neural networks
Odour recognition
QCM sensors
Neural networks
Data preprocessing
Odour recognition
QCM sensors
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Summary:An experimental comparison of linear and non-linear pre-processing methods for olfactory data is made. The original data are formed by 280 values of reaction from six quartz crystal microbalance (QCM) sensors taken at 1 s intervals. Data vectors are processed with the non-linear maximum filter or by the linear averaging filter and are used as inputs of a feedforward neural network using the back-propagation learning rule, one hidden layer containing from 5 to 15 neurons. The filter window size is 5–10. The learning set is composed of 60 sensor reactions for six types of cologne. The neural network correctly classifies 82–86% of independent examples. The usage of the maximum filter with a small window size allows an increase of the classification rate by 3–5%. The best results (86%) are obtained when only first 50 measurements of sensor reaction are used.
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ISSN:0925-4005
1873-3077
DOI:10.1016/j.snb.2004.05.047