@ARTICLE{Kalinowski_Paweł_Efficiency_2013,
 author={Kalinowski, Paweł and Woźniak, Łukasz and Strzelczyk, Anna and Jasinski, Piotr and Jasiński, Grzegorz},
 number={No 3},
 journal={Metrology and Measurement Systems},
 pages={501-512},
 howpublished={online},
 year={2013},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 abstract={Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling},
 type={Artykuły / Articles},
 title={Efficiency of Linear and Non-Linear Classifiers for Gas Identification from Electrocatalytic Gas Sensor},
 URL={http://journals.pan.pl/Content/90171/PDF/Journal10178-VolumeXX%20Issue3_16.pdf},
 doi={10.2478/mms-2013-0043},
 keywords={electrocatalytic sensor, cyclic voltammetry, data pre-processing, Support Vector Machine, Partial Least Squares Discriminant Analysis},
}