Glass fragments found in crime scenes may constitute
important
forensic evidence when properly analyzed, for example, to determine
their origin. This analysis could be greatly helped by having a large
and diverse database of glass fragments and by using it for constructing
reliable machine learning (ML)-based glass classification models.
Ideally, the samples that make up this database should be analyzed
by a single accurate and standardized analytical technique. However,
due to differences in equipment across laboratories, this is not feasible.
With this in mind, in this work, we investigated if and how measurement
performed at different laboratories on the same set of glass fragments
could be combined in the context of ML. First, we demonstrated that
elemental analysis methods such as particle-induced X-ray emission
(PIXE), laser ablation inductively coupled plasma mass spectrometry
(LA-ICP-MS), scanning electron microscopy with energy-dispersive X-ray
spectrometry (SEM-EDS), particle-induced Gamma-ray emission (PIGE),
instrumental neutron activation analysis (INAA), and prompt Gamma-ray
neutron activation analysis (PGAA) could each produce lab-specific
ML-based classification models. Next, we determined rules for the
successful combinations of data from different laboratories and techniques
and demonstrated that when followed, they give rise to improved models,
and conversely, poor combinations will lead to poor-performing models.
Thus, the combination of PIXE and LA-ICP-MS improves the performances
by ∼10–15%, while combining PGAA with other techniques
provides poorer performances in comparison with the lab-specific models.
Finally, we demonstrated that the poor performances of the SEM-EDS
technique, still in use by law enforcement agencies, could be greatly
improved by replacing SEM-EDS measurements for Fe and Ca by PIXE measurements
for these elements. These findings suggest a process whereby forensic
laboratories using different elemental analysis techniques could upload
their data into a unified database and get reliable classification
based on lab-agnostic models. This in turn brings us closer to a more
exhaustive extraction of information from glass fragment evidence
and furthermore may form the basis for international-wide collaboration
between law enforcement agencies.