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Classification of Glass Fragments Based on Elemental Composition and Refractive Index *
Author(s) -
Zadora Grzegorz
Publication year - 2009
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/j.1556-4029.2008.00905.x
Subject(s) - support vector machine , refractive index , classification scheme , naive bayes classifier , mathematics , computer science , analytical chemistry (journal) , materials science , optics , artificial intelligence , chemistry , physics , chromatography , machine learning
The aim of this study was to assess the efficiency of likelihood ratio (LR)‐based measures when they are applied to solving various classification problems for glass objects which are described by elemental composition, and refractive index (RI) values, and compare LR‐based methods to other classification methods such as support vector machines (SVM) and naïve Bayes classifiers (NBC). One hundred and fifty‐three glass objects (23 building windows, 25 bulbs, 32 car windows, 57 containers, and 16 headlamps) were analyzed by scanning electron microscopy coupled with an energy dispersive X‐ray spectrometer. Refractive indices for building and car windows were measured before (RI b ), and after (RI a ) an annealing process. The proposed scheme for glass fragment(s) classification demonstrates some efficiency, although the classification of car windows ( c ) and building windows ( w ) must be treated carefully. This is because of their very similar elemental content. However, a combination of elemental content and information on the change in RI during annealing (ΔRI = RI a −RI b ) gave very promising results. A LR model for the classification of glass fragments into use‐type categories for forensic purposes gives slightly higher misclassification rates than SVM and NBC. However, the observed differences between results obtained by all three approaches were very similar, especially when applied to the car window and building window classification problem. Therefore, the LR model can be recommended because of the ease of interpretation of LR‐based measures of certainty.