Abstract

The optimization of a sensor array for a concrete analytical task is usually concerned with choosing a set of sensors to provide the best classification. In this work, a method for the prediction of the quality of classification by evaluation of the uniqueness of the raw experimental data is proposed. The key feature of the method is the presentation of the response of array as a function of the responses of its sensors. The dispersion of those functions serves as quantitative measure of uniqueness of the experimental data for a given set of analytes.The efficiency of the approach has been successfully demonstrated using both simulated and experimental data obtained from the array of three mass-sensitive sensors. The best conformity of the classification efficiency in cluster analysis with results obtained in the framework of the proposed approach is observed in the case of Langmuir-type adsorption processes.

Autore Pugliese

• Siciliano Pietro Aleardo , Capone Simonetta

Tutti gli autori

• Burlachenko J.V.; Snopok B.A.; Capone S.; Siciliano P.

Titolo volume/Rivista

IEEE sensors journal

Anno di pubblicazione

2011

ISSN

1530-437X

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile