Examination of Firearms Review: 2007 to 2010 - Interpol

Continuing the work of Curran et al. (94), Campbell and Curran (95) introduce a
“permutation testing approach” to the interpretation of elemental analysis data
from glass evidence. In previous work, the authors suggested using Hotelling’s
T 2 to evaluate multivariate data that may be used in a Bayesian calculation.
Criticisms of this approach are addressed; namely, the assumption of normality,
and the relatively large sample size not necessarily available to the forensic
scientist. The authors had some success removing these constraints using a
shrinkage estimator. The reader is directed to regard this as a “pre-screening”
tool and not for interpretation for the courts.
Zadora and Neocleous (96) and Zadora (97) employ a Bayesian approach for
classification of glass fragments into use-type categories using RI and SEM-EDS
data. Support vector machines, naïve Bayes classifiers, and Bayesian networks
were again assessed as well as the performance of the likelihood ratio models
when considering between- and within-object variability. A model that takes both
into consideration performs better than a model using between-object variability
only. The accuracy of the classification is highly dependent upon glass type, but
in general, the combination of SEM-EDS and change in RI values with annealing
increased anywhere from 3% to greater than 10% over SEM-EDS and preannealing
RI data alone. Caution should be taken when using this type of data
alone for classification purposes as the values for several glass types (e.g.
architectural and automotive) tend to overlap. Classification based on preannealed
RI values alone should not be attempted. Likelihood ratios from these
data are usually close to one, giving very limited support to either the
prosecution’s or defense’s hypothesis.
Jensen and Shen (98) explore the use of statistical feature selection as an aid to
Bayesian analysis of glass. In fact, they aim to remove the need for expert
knowledge in feature selection and advocate univariate data analysis for
likelihood ratio calculations. While the use of automated feature techniques may
be of use in evidence evaluation, it is unadvisable to rely on statistical modeling
of a likelihood ratio without sufficient knowledge of the underlying analytical data.
Zadora and Ramos (99) study the influence of adequate population databases
for likelihood ratio calculations. Databases assessed are based on RI and SEM-
EDS data. This study was also presented at the 2009 EFS meeting in Glasgow,
Scotland (100) and the 15 th Annual meeting of the EPG in St. Gallen, Switzerland
(101).
This concludes the Glass Report for 2010. Future papers and presentations will
be summarized in the next report.
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