Feature selection for the discrimination between pollution types with partial least squares modelling

Abstract

Discrimination between different types of pollution was carried out with partial least squares (PLS). The data sets concern steelworks and galvanization sludges and urban sludges coming from two different depuration steps. A method of feature selection based on the closed from of the PLS model is proposed and satisfactory classification rates were achieved (correct classification rate values between 94 and 70%). The results show that feature selection can improve the performance of the PLS classification and reduce the number of analytical determinations to be performed. Outlying samples were detected and omitted before the classification.