Statistical Analysis of GSR Particles Morphometry Using the CART Method

According to ASTM E1588-20, gunshot residue (GSR) particles can be unequivocally identified through chemical and morphometric analysis using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS), the gold standard technique for GSR detection. Recent studies have reported the presence of characteristic GSR particles—containing lead (Pb), barium (Ba), and antimony (Sb)—on vehicle occupants exposed to airbag deployment, underscoring the need for complementary analytical approaches. While elemental composition remains the primary criterion for GSR identification, morphometric analysis enhances the ability to differentiate GSR from other environmental particles. Furthermore, detailed characterization of GSR particle morphology may assist in determining the type of firearm used in a shooting incident. This study systematically analyzed characteristic GSR particles originating from four Brazilian-manufactured ammunition, establishing an initial framework for differentiating between two classes of firearms (short and long) based on morphometric features using the Classification and Regression Tree (CART) method. CART is well-suited for scenarios where interpretability and ease of implementation are priorities. Two short firearms—Taurus G2C pistol (0.40 caliber) and Glock G23 pistol (9 mm caliber) and two long firearms—Colt M16A2 rifle (5.56 mm caliber) and IMBEL FAL rifle (7.62 mm caliber) were tested: Ammunition types included CBC 0.40 S&W CSCV 160 gr, CBC 9 mm copper bullet (batch BNC10), CBC 5.56 mm AXO46 (batch A0142946), and CBC 7.62 × 51 mm Common. Morphometric analysis revealed distinct variations in characteristic GSR particle profiles across different ammunition calibers. A new four-category classification system for characteristic GSR particles was developed, with 57% identified as regular spheroids. Using CART analysis, a statistical model achieved 76% accuracy in distinguishing between short and long firearms based on morphometric parameters, particularly circularity and Feret diameter. Further research with expanded datasets and alternative predictive methods is recommended to enhance model performance and generalizability. These findings reinforce the potential of morphometric classification as a complementary tool in forensic ballistics.