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Performance of Machine Olfaction: Effect of Uniqueness of the Initial Data and Information Coding on the Discrimination Ability of Multisensor Arrays

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.