Some of
history’s most notorious unsolved murder crimes could be laid bare thanks to
new forensic research led at Northumbria University, in Newcastle.
Dr Matteo Gallidabino, Senior Lecturer in Forensic
Science at Northumbria University, working with colleagues at King’s
College London and the Universities of Lausanne, Santiago de Compostela and
Rome (La Sapienza), have
revealed that machine learning – a field of artificial intelligence – could be
used to determine which ammunition, and ultimately which firearm, was responsible
for a particular gunshot from the residue it left behind.
Their findings have been published in the
Royal Society of Chemistry’s Analyst journal – the home of fundamental
discoveries, inventions and applications in analytical and bioanalytical
sciences.
“Machine learning uses a series of algorithms to model complex data
relationships” explains Dr Gallidabino. “Through careful fine-tuning, these can
be applied to predict important characteristics of the ammunition used in a
particular shooting event from those of the respective gunshot residue (GSR)
deposited on surrounding surfaces or items, such as spent cases, wounds and,
potentially, also the shooter’s hands.”
This is a ground-breaking progression when compared to techniques currently
available in GSR analysis. Indeed, the complex computer statistics implemented
by the research team allow identification of the ammunition responsible for the
different gunshot traces left at a crime scene and eventual association of such
traces, with unprecedented accuracy. Both capabilities are currently challenging
using traditional forensic methods.
With further developments of the suggested approach, new investigative
leads could be rapidly pursued to avoid similar unsolved murders of the past,
like Jill Dando in 1999* and the notorious Bloody Sunday killings of 1972.
“After Bloody
Sunday, the problem was to determine if gunshots were fired by civilians or
military staff” states Dr Gallidabino. “The investigators found large amounts
of GSR all over victims and concluded that these resulted from shooting
activities. It was later established, however, that these were likely due to
the secondary, post-event transfer of contaminations from military staff –
whose hands were rich with GSRs – to dead bodies. Small amounts of GSR, indeed,
may be transferred by prolonged contacts with contaminated surfaces, such as
those that took place when soldiers helped transport victims to the hospital
after the event.
“If techniques
such as those we are developing now were available at the time, they could have
been used to determine if GSRs came from civilian ammunition or military fire,
which would have been a critical piece of evidence.”
Dr Gallidabino specialises in statistical modelling and
machine learning techniques for forensic applications. He personally developed
and tested both the innovative chemical technique and the mathematical models
used in the approach, after firing a range of ammunition. After
collecting the gun cartridges, he analysed them, and particularly the volatile
part of the GSR, before turning his attention to the original smokeless powders.
From here, he was able to establish a relationship between the ammunition and
the residue, with the same statistical methods used by computer scientists to
train robots.
Following on
from this, the research team has called for this unique method to be applied
much more widely in the field of forensic science and, more generally, analytical
chemistry. “The benefits are countless” Dr Gallidabino said. “They may even
extend to other fields in analytical sciences that routinely encounter
changeable chemical traces, such as the analysis of improvised explosive
devices, arson accelerants and environmental pollutants.”
Dr Leon
Barron, Senior Lecturer in Forensic Science from King’s College London, added:
“The fusion of state-of-the-art laboratory analysis with computer-based machine
learning will enable us to capitalise on the vast amounts of data we now
generate to make ground-breaking advances like this more frequently. In
forensic science, and often given the varied scenarios and sequences of events
involved, machine learning represents one of the most promising ways to make
sense of evidence more rapidly to support the Criminal Justice System.”
*Jill Dando
On April 26,
1999, the 38-year-old BBC star was shot dead on the doorstep of her home in
Fulham, West London in what remains one of the UK's most high-profile unsolved
murders. Barry George, who lived a few minutes from Jill's house, was jailed
for eight years for her murder but was cleared after a retrial in 2008
following concerns raised over forensic evidence. The case remains open.
Having more
knowledge about the source of GSR at the time of the murder could have been
useful, according to Dr Gallidabino and the rest of the research team.
“One single
GSR particle was found in the pocket of the coat of Barry George (the suspect)”
he says. “This particle was shown to have a really similar composition to those
found on the victim, Jill Dando, according to techniques available at time. As
no approved method existed to compare GSR compositions on different surfaces,
however, this evidence was strongly contested. With our approach, we hope in
the future to provide robust tools to law enforcement agencies to more
efficiently address this kind of situation.”
Northumbria's Department of Applied Sciences has an exciting and extensive portfolio of subjects including biology, biomedical sciences, chemistry, forensic science, food and nutritional sciences. The department is actively engaged in research and enterprise; academics within the department are internationally and nationally renowned within their fields This wide range of academic expertise not only ensures quality teaching but means our graduates have good insight into the skills and attributes valued by employers.
