Use of pattern recognition for signatures generated by laser desorption–ion mobility spectrometry of polymeric materials

Abstract

This paper describes the use of an artificial neural network (ANN) for pattern recognition of ion mobility signatures of polymeric materials produced by the method known as laser ablation–ion mobility spectrometry (LA-IMS). This technique has been used to examine a wide range of polymer-based materials. Mobilization of the sample was achieved using a laser and the ion mobility spectrometer gave characteristic signatures for materials introduced as vapours. The application of the ANN to the analysis of the IMS signatures has resulted in a novel method for discriminating polymeric materials. Initial pattern recognition results obtained from several polymers showed an accuracy of 100% in both the test and use domains. Negative ion mobility signatures are presented to demonstrate how the signatures were treated before analysis using the ANN. Future development of the technique is discussed, including the use of mass spectrometry to identify the species produced by the action of the laser and the use of on-line neural network pattern recognition techniques. The ANN approach is applicable to other process and environmental monitoring situations.