A Review of Analytical and Chemometric Strategies for Forensic Classification of Homemade Explosives

Abstract

Homemade explosives (HMEs), commonly used in improvised explosive devices (IEDs), present a significant forensic challenge due to their chemical variability, accessibility and adaptability. Traditional forensic methodologies often struggle with environmental contamination, complex sample matrices and the non-specificity of precursor residues. Recent advances in analytical techniques and chemometric methods have enhanced the detection, classification and interpretation of explosive residues. Infrared (IR) spectroscopy and gas chromatography–mass spectrometry (GC–MS) have seen improvements in spectral resolution and real-time detection capabilities, allowing for more accurate differentiation of explosive precursors. Thermal analysis techniques, such as thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC), now provide refined kinetic modelling to assess the decomposition pathways of unstable energetic materials, improving forensic risk assessments. Additionally, x-ray diffraction (XRD) has contributed to forensic material sourcing by distinguishing between industrial-grade and improvised explosive formulations. Chemometric approaches, including principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA), have revolutionized forensic data analysis by improving classification accuracy and enabling automated identification of explosive components. Advanced machine learning models are being integrated with spectral datasets to enhance real-time decision-making in forensic laboratories and portable field devices. Despite these advancements, challenges remain in adapting laboratory-based techniques for field deployment, particularly in enhancing the sensitivity and robustness of portable analytical instruments. This review critically evaluates the latest developments in forensic analytical chemistry, highlighting strengths, limitations and emerging strategies to improve real-world HME detection and classification.