Hierarchical classification and matching of mid-infrared spectra of paint samples for forensic applications

Abstract

A novel fast and automatic methodology for the hierarchical classification and similarity matching of mid-infrared spectra of paint samples based on the principles of Soft Independent Modelling of Class Analogy (SIMCA) and on the definition and properties of the Mahalanobis distance is here proposed. This approach was tested in a so-called market study (i.e., targeting products largely accessible to the general public and conceived for a considerably wide range of usages) conducted across the surroundings of the city of Lille, in France, and has permitted not only to successfully achieve the chemical characterisation of most of the analysed samples but also to discover specific commonality patterns among specimens sharing the same chemical features.