Forensic paint evidence plays an important role in criminal investigations, particularly in cases involving vehicular crimes, burglaries, and hit-and-run incidents. Paint examinations rely on a sequence of analytical techniques based on microscopical and instrumental analysis methods to compare unknown specimens with known reference samples. In current practice, results from these techniques are evaluated sequentially and largely subjectively, making it difficult to consistently assess the combined strength of the evidence or to ensure reproducibility across laboratories and examiners.
This research addressed a critical need identified by the trace evidence community (i.e., OSAC (trace) Materials subcommittee and the American Society of Trace Evidence Examiners): the development of objective, transparent, and scientifically grounded methods for evaluating trace evidence. The study investigated the use of data fusion techniques to integrate results from multiple analytical methods into a unified evaluative framework for forensic paint examinations. Four representative paint sample sets—including architectural, spray, automotive refinishing, and multilayer automotive paints—were analyzed using light microscopy, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), ultraviolet-visible microspectrophotometry (UV-Vis MSP), and Raman spectroscopy. More than 8,500 individual measurements were acquired and evaluated using multiple data fusion strategies and classification approaches.
The findings demonstrate that high-level data fusion methods, which combine the outcomes of individual analytical techniques rather than raw data alone, consistently produced the most accurate and robust classifications. Ensemble and chemometric approaches, particularly those using majority-vote decision rules and random forest models, improved the classification of paint sources and reduced the impact of ambiguous or low-information results from individual techniques. Importantly, integrating multiple methods compensated for the known limitations of certain analyses when used in isolation.
The study also identified systematic redundancies as well as complementarities among commonly used paint examination techniques. Some methods frequently captured overlapping information about the same paint components, whereas others showed greater differentiation potential. These findings have direct policy implications, as they support the development of more efficient, evidence-based analytical sequences that reduce unnecessary testing while preserving evidentiary value.
Overall, this research—to be considered a first phase—demonstrates that data fusion provides a viable pathway toward greater standardization, objectivity, and in trace evidence examinations. Indeed, the results lay essential groundwork for future investigation of probabilistic reporting approaches, such as likelihood ratios, and for the development of policy guidance and best practices within U.S. forensic laboratories.
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