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.

Keywords: chemometric methods; explosive residue detection; forensic analysis; gas chromatography–mass spectrometry (GC–MS); homemade explosives (HMEs); infrared spectroscopy (IR).

FIGURE 1

Graphical abstract illustrating the forensic source determination of ammonium nitrate (AN). Source: Figure adapted from D'Uva et al. [16], Copyright 2022, Elsevier.

FIGURE 2

Comparison of FTIR (black) and O‐PTIR (pink) spectra for high explosives (C‐4, RDX, PETN and TNT), showing spectral matching. FTIR, Fourier‐transform infrared spectroscopy; O‐PTIR, optical‐photothermal infrared; PETN, pentaerythritol tetranitrate; RDX, Royal Demolition Explosive; TNT, 2,4,6‐trinitrotoluene. Source: Figure adapted from Banas et al. [19], Copyright 2020, ACS Analytical Chemistry.

FIGURE 3

Chemometric model for NIR‐based forensic identification of intact energetic materials, integrating an explosives reference matrix, LDA classification and NAS‐based spectral reconstruction. Source: Figure adapted from Van Damme et al. [20], published under the CC BY license (MDPI, Sensors).

FIGURE 4

Schematic of MEMS‐TGA technology showing (A) miniaturized TGA components, (B) real‐time mass loss detection and (C) improved heating efficiency. MEMS‐TGA, micro‐electromechanical systems‐based thermogravimetric analysis. Source: Figure adapted from Yao et al. [22], Copyright 2021, ACS Analytical Chemistry.

FIGURE 5

DSC curves of (A) RDX and (B) PETN with various polymeric binders, showing thermal compatibility and decomposition behaviour. DSC, differential scanning calorimetry; PETN, pentaerythritol tetranitrate; RDX, Royal Demolition Explosive. Source: Figure adapted from Nguyen et al. [25], published under the CC BY license (MDPI, Polymers).

FIGURE 6

Pictograms for sample introduction in portable GC–MS systems, ensuring accuracy in field analysis. Source: Figure adapted from Katilie et al. [31], Copyright 2019, SAGE Publications, Applied Spectroscopy.

FIGURE 7

PCA plots showing the classification of ANFO explosive samples based on diesel composition: (A) n‐alkanes, isoprenoids, and fatty acid methyl esters; (B) n‐alkanes and isoprenoids; (C) fatty acids methyl esters; and (D) isoprenoids and fatty acid methyl esters. Each set represents a diffrent selection of diesel compounds used for classification. The correctly classified clusters are shown in circles, highlighting the effectiveness of chemometric analysis in differentiating ANFO formulations. Source: Figure adapted from Suppajariyawat et al. [35], Copyright 2019, Elsevier, Forensic Science International.

FIGURE 8

XRD analysis of coal dust explosion residues, highlighting mineralogical transformations. Source: Figure adapted from Qian et al. [37], Copyright 2018, Elsevier, International Journal of Hydrogen Energy.

FIGURE 9

Workflow for the forensic detection and analysis of homemade explosives (HMEs).