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.

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Analytical Science Advances
REVIEW
A Review of Analytical and Chemometric Strategies for
Forensic Classification of Homemade Explosives
Abdulrahman Aljanaahi1Muhammad K. Hakeem2Abdulla Aljanaahi1Iltaf Shah2
1Dubai Police General Headquarters, Dubai, UAE 2Department of Chemistry, College of Science, United Arab Emirates University (UAEU), Al Ain, UAE
Correspondence: Iltaf Shah (altafshah@uaeu.ac.ae)
Received: 6 November 2024 Revised: 7 March 2025 Accepted: 11 March 2025
Funding: This study was supported by United Arab Emirates University and Dubai Police General Headquarters, UAE.
Keywords: chemometric methods | explosive residue detection | forensic analysis | gas chromatography–mass spectrometry (GC–MS) | homemade explosives
(HMEs) | infrared spectroscopy (IR)
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.
1 Introduction
The proliferation of improvised explosive devices (IEDs) has
emerged as a significant security threat, posing substantial
risks to public safety, infrastructure and national security. The
accessibility of explosive precursor materials, coupled with the
ease of assembly, makes IEDs a versatile and widespread threat
that transcends geopolitical and economic boundaries. Their use
is not limited to conflict zones but extends to urban and civil-
ian environments, complicating counterterrorism and forensic
efforts. The consequences of IEDs are far-reaching, impacting
not only immediate casualties but also economic stability, envi-
ronmental safety and international diplomatic relations [1–3].
Homemade explosives (HMEs), a critical subset of IEDs, present
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly
cited.
© 2025 The Author(s). Analytical Science Advances published by Wiley-VCHGmbH.
Analytical Science Advances, 2025; 6:e70010
https://doi.org/10.1002/ansa.70010
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unique challenges due to their diverse chemical compositions
and ease of synthesis. Recent studies categorize HMEs based
on their precursor materials, including peroxide-based explosives
(e.g., triacetone triperoxide, TATP), nitrate-based explosives (e.g.,
ammonium nitrate [AN] fuel oil, ANFO) and chlorate-based
explosives (e.g., potassium chlorate mixtures). Each category
exhibits distinct thermal stability, decomposition behaviour and
structural properties, complicating forensic investigations [4–7].
Additionally, the widespread availability of precursor chemicals
in household, agricultural and industrial products makes the
tracking and regulation of these materials highly challenging.
Environmental contamination, variability in synthesis methods
and the presence of impurities further complicate forensic anal-
ysis, making accurate detection and classification difficult. The
forensic analysis of HMEs is vital for identifying their chemical
signatures, linking residues to their sources and developing pre-
ventive measures against their misuse. The increasing frequency
of terrorist activities and criminal use of HMEs underscores the
urgency of refining analytical methodologies for their detection
and characterization. Consequently, advancements in analytical
chemistry have become paramount in ensuring accurate foren-
sic identification, origin-tracing and classification of explosive
materials [8].
2 Advances in Analytical Technologies
The forensic detection and analysis of HMEs require advanced
analytical techniques capable of distinguishing explosive com-
pounds from benign materials with high sensitivity and speci-
ficity. Infrared (IR) spectroscopy and gas chromatography–mass
spectrometry (GC–MS) have been widely used for identifying
the chemical signatures of HMEs [9]. These techniques provide
molecular-level insights into explosive compositions, enabling
forensic experts to differentiate among various explosive for-
mulations. However, despite their effectiveness, current forensic
methodologies face challenges related to environmental contam-
ination, matrix effects and the reproducibility of results in field
conditions.
Innovative tools, such as laser-driven thermal reactors, near-
infrared (NIR) spectroscopy and high-resolution mass spec-
trometry (HRMS), are being explored to enhance the forensic
detection of explosives. However, traditional analytical methods
still struggle to differentiate HMEs from chemically similar non-
explosive substances, leading to false positive or inconclusive
results. Environmental contamination further complicates foren-
sic investigations by altering chemical signatures and introducing
variability in spectral data [10–14].
Another major challenge is the limited portability of sophisti-
cated forensic instruments. Although laboratory-based methods
provide high accuracy, the need for real-time, field-deployable
forensic tools is growing. The integration of chemometric
approaches with portable analytical systems has the potential
to enhance data interpretation and classification accuracy in
dynamic environments. Additionally, ensuring the robustness
and reproducibility of forensic methods across different opera-
tional conditions remains a key concern. Recent reviews, such
as López-López and García-Ruiz [15], have discussed analytical
techniques for general explosives detection, focusing on trace
detection and safety protocols [15]. However, this review uniquely
emphasizes the fusion of analytical techniques with chemometric
methodologies tailored for HME classification. By addressing
critical challenges, such as method reproducibility, field appli-
cability and data processing limitations, this review aims to
provide a structured roadmap for advancing forensic method-
ologies. The discussion extends beyond conventional detection
methods to highlight the integration of artificial intelligence (AI),
machine learning (ML) and advanced chemometric techniques
for improving forensic capabilities.
Given the persistent and evolving threats posed by HMEs, con-
tinuous research is essential for refining detection technologies,
improving field applicability and enhancing forensic reliability.
This review critically evaluates recent advancements in analyt-
ical and chemometric techniques for forensic HME analysis,
identifies existing limitations and proposes future research direc-
tions aimed at strengthening forensic capabilities in real-world
applications.
2.1 IR in Forensic Analysis of Explosive
Materials
IR spectroscopy is a crucial analytical technique in forensic
science, widely used for detecting and characterizing explo-
sive materials. By analysing molecular vibrations, IR spec-
troscopy offers a non-destructive and highly specific method
for forensic investigations. Advanced IR methodologies such as
Fourier-transform infrared (FTIR) spectroscopy, attenuated total
reflectance FTIR (ATR-FTIR) spectroscopy, optical-photothermal
infrared (O-PTIR) spectromicroscopy and NIR spectroscopy have
enhanced forensic capabilities in detecting and classifying explo-
sives with improved sensitivity and specificity [16–18, 15, 19, 20].
The samples analysed in many studies underwent preparation
steps to improve spectral accuracy. These steps included drying,
homogenizing and filtering to remove contaminants and ensure
consistency across measurements.
Although FTIR spectroscopy provides high-resolution molecular
fingerprints, ATR-FTIR is increasingly favoured for its superior
surface sensitivity and minimal sample preparation require-
ments. In a study by D’Uva et al. [16], ATR-FTIR, in conjunction
with trace elemental analysis via inductively coupled plasma
mass spectrometry (ICP-MS) and chemometric modelling, was
employed for a comprehensive forensic analysis of AN prod-
ucts. The study examined both pure AN and homemade AN
formulations, achieving a 92.5% classification accuracy using a
discriminant function model. Further refinement through step-
wise linear discriminant analysis (LDA) and principal component
analysis (PCA) enabled clear differentiation between pure and
homemade AN samples, with ATR-FTIR sulphate peaks and trace
elemental variations emerging as key discriminators. Although
some overlap in sample clusters was noted, the integration of
ATR-FTIR, ICP-MS and chemometric tools provided a robust
and validated approach for forensic source determination of
AN, essential in explosives investigations [16]. A schematic
representation of this forensic workflow is depicted in Figure 1,
highlighting the analytical synergy among ATR-FTIR, ICP-MS
and multivariate data analysis.
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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.
Another important development is the use of multivariate anal-
ysis techniques in post-blast residue examination, where residue
particles were dissolved and filtered to enhance spectral clarity.
Banas et al. [17] demonstrated the effectiveness of hierarchical
cluster analysis (HCA) and PCA in enhancing the spectroscopic
classification of explosive residues. By employing chemometric
approaches, forensic experts can distinguish explosive compo-
nents from environmental contaminants with improved precision
[17]. Ewing and Kazarian [18] elaborated on the value of IR
spectroscopy and spectroscopic imaging in forensic science. Their
review underscored the potential of FTIR spectroscopic imaging
in various modes, including transmission, external reflection and
ATR, for forensic applications. These techniques allow detailed
chemical profiling of forensic samples. Such advancements have
enabled forensic investigators to detect explosive residues even
within fingerprint evidence, aiding in suspect identification [18].
López-López and García-Ruiz [15] provided a comprehensive
review of recent advances in IR and Raman spectroscopy for
explosives analysis. Their study highlighted the critical role of
these techniques in homeland security and environmental moni-
toring, particularly in the trace detection and characterization of
explosive substances [15].
Furthermore, O-PTIR spectromicroscopy, as demonstrated by
Banas et al. [19], provides a non-destructive approach for detect-
ing high-explosive materials within fingerprints. Figure 2com-
pares O-PTIR and FTIR spectra, highlighting O-PTIR’s superior
forensic potential in identifying high explosives.
This novel method overcomes the limitations of traditional IR
techniques by offering higher spatial resolution and eliminat-
ing fluorescence interference, making it an ideal candidate for
forensic investigations [19]. One of the major limitations of
laboratory-based IR techniques is their inability to be deployed
in the field. Recent advancements in portable NIR spectroscopy
have bridged this gap. Van Damme et al. [20]demonstratedthe
feasibility of NIR spectroscopy combined with multivariate data
analysis for on-site identification of intact energetic materials,
FIGURE 2 Comparison of FTIR (black) and O-PTIR (pink) spectra
for high explosives (C-4, RDX, PETN and TNT), showing spectral match-
ing. 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.
providing real-time forensic insights, as shown in Figure 3. This
approach enables real-time and non-invasive detection of a broad
spectrum of explosive materials, significantly enhancing on-site
forensic capabilities [20].
Despite these advancements, forensic IR spectroscopy faces
challenges in analysing complex mixtures, quantifying trace
explosives and addressing spectral overlaps caused by contami-
nants in post-blast residues. Additionally, portable IR technolo-
gies still require improvements in sensitivity and robustness to
match laboratory-based techniques. A comparative analysis of
these IR spectroscopy techniques, including their advantages
and limitations in forensic explosive analysis, is summarized in
Table 1. Future research should focus on enhancing chemometric
integration in IR-based forensic analysis for better spectral inter-
pretation, developing miniaturized, high-sensitivity portable IR
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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).
TABLE 1 Comparative analysis of infrared spectroscopy techniques in forensic explosive analysis.
IR technique Advantages Limitations
FTIR High-resolution molecular fingerprinting;
well-established forensic method
Requires sample preparation; interference
from environmental contaminants
ATR-FTIR Minimal sample preparation; high surface
sensitivity; effective for solid-phase analysis
Limited penetration depth; sensitivity varies
based on sample homogeneity
O-PTIR High spatial resolution; overcomes fluorescence
issues; suitable for fingerprint analysis
Requires advanced instrumentation; not
widely available in forensic labs
NIR spectroscopy Portable, rapid on-site detection; effective for field
applications
Lower spectral resolution compared to FTIR;
requires chemometric models for data
interpretation
Abbreviations: ATR-FTIR, attenuated total reflectance Fourier-transform infrared; FTIR, Fourier-transform infrared; IR, infrared; NIR, near-infrared; O-PTIR,
optical-photothermal infrared.
devices for on-site explosives detection and improving quantita-
tive IR methods for forensic validation and legal admissibility.
2.2 Thermogravimetric Analysis (TGA) in
Forensic Analysis of Explosives
TGA is a fundamental thermal technique in forensic inves-
tigations, widely utilized for assessing the thermal stability,
decomposition behaviour and composition of explosive materi-
als. By continuously measuring the weight loss of a substance
under controlled temperature increases, TGA provides crucial
data for understanding the thermal degradation pathways of
explosives. This capability is essential in forensic science for clas-
sifying explosives based on decomposition kinetics, identifying
unknown explosive materials and ensuring safe handling and
storage protocols. The technique has been particularly valuable
in differentiating commercial and HMEs by examining their
thermal signatures under controlled atmospheres [21–23].
Recent advancements in TGA technology have significantly
enhanced its forensic applications, particularly with the devel-
opment of micro-electromechanical system-based TGA (MEMS-
TGA). Unlike conventional TGA, which requires milligram-scale
samples, MEMS-TGA enables the analysis of nanogram quanti-
ties, reducing sample consumption while maintaining analytical
precision. Yao et al. [22] reported that MEMS-TGA, through rapid
controlled heating and improved temperature modulation, allows
forensic investigators to analyse explosives with unprecedented
sensitivity. MEMS-TGA offers an improved thermal resolution
of approximately 0.001◦C, compared to the 0.1◦C resolution of
conventional TGA. Additionally, it enables rapid heating rates
up to 10,000◦C/min, significantly improving the detection of
low-energy decomposition events in trace explosive residues,
a capability crucial for forensic differentiation of similar com-
pounds. Furthermore, MEMS-TGA enhances laboratory safety
by minimizing the risk of accidental detonation, as it requires
only minute amounts of energetic material [22]. Figure 4illus-
trates the working principle of MEMS-TGA, highlighting its
microcantilever-based resonator, miniaturized heating system,
and real-time mass loss detection. This schematic demonstrates
how MEMS-TGA achieves superior thermal resolution and foren-
sic sensitivity, making it a groundbreaking advancement in
forensic TGA applications.
In addition to MEMS-based advancements, combined TGA and
differential scanning calorimetry (TGA-DSC) have proven highly
effective in forensic investigations. Zeman et al. [21]usedTGA
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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.
to examine the decomposition kinetics of commercial explosives,
revealing a linear correlation between the onset temperature
of degradation and differential thermogravimetry (DTG) peak
positions. This study demonstrated that TGA data could be
leveraged to estimate the explosion temperatures of various
formulations, aiding forensic experts in reconstructing explosive
events. To ensure reproducibility, the sample preparation proto-
col involved grinding and homogenizing explosive compounds,
which minimized variability in decomposition rates. Such find-
ings underscore the role of TGA in predicting the thermal
behaviour of explosive compositions under extreme conditions,
contributing to forensic reconstruction efforts [21].
Furthermore, thermal analysis techniques such as TGA-DSC
have proven to be quick and safe methods for investigating
the thermal properties of primary explosives and other high-
energy materials. TGA sample preparation often involves drying
to remove residual moisture and stabilizing the sample under
inert gas flow to minimize oxidative interference during thermal
analysis. These techniques provide critical data on the thermal
characteristics of such materials, thereby contributing to safer
handling practices and a deeper understanding of their stability
[23].
Another critical aspect of forensic TGA applications is its use in
primary explosives and highly energetic materials. Pniewski et al.
[23] applied TGA to investigate the thermal properties of selected
primary explosives, highlighting the varying stability of different
energetic compounds under controlled heating conditions. The
results provided forensic scientists with valuable insights into
the decomposition pathways and ignition thresholds of these
materials, allowing for improved safety guidelines in explosive
storage and transport. Additionally, this study emphasized the
importance of inert gas environments during thermal analysis
to prevent unwanted oxidation, which could lead to erroneous
conclusions.
Despite these technological advancements, forensic TGA still
faces several challenges, including interferences from envi-
ronmental contaminants, the complexity of multi-component
explosives and the need for standardized forensic protocols. For
instance, explosives mixed with soil, fuels or other environmental
debris can alter thermal degradation pathways, leading to over-
lapping peaks in DTG curves that obscure key forensic signatures.
Such interferences necessitate careful sample preparation, such
as controlled desorption techniques, to isolate the explosive
component from background noise.
The integration of chemometric methods in TGA data analysis
has emerged as a promising solution, allowing forensic scien-
tists to extract meaningful information from complex thermal
signatures. Compared to DSC, which provides insights into heat
flow and enthalpic transitions, TGA uniquely allows for mass
loss tracking, making it particularly suitable for differentiating
formulations based on thermal decomposition rates. However,
unlike differential thermal analysis (DTA), TGA does not provide
direct exothermic or endothermic transition data, meaning that
complementary DSC or DTA techniques are often required for a
complete forensic profile of an explosive material. For instance,
forensic identification of binary explosive mixtures (e.g., TATP-
based compositions) often requires both TGA and DSC/DTA to
distinguish stabilizer loss from explosive decomposition [24].
To further strengthen the applicability of TGA in forensic science,
future research should prioritize enhancing real-time chemo-
metric integration in TGA-based forensic analysis to improve
spectral interpretation and classification accuracy. Additionally,
efforts should be directed towards developing miniaturized,
high-sensitivity portable TGA devices, which would enable on-
site forensic examination of explosives while reducing logistical
constraints associated with laboratory-based methods. Another
critical aspect is the standardization of TGA forensic protocols
to ensure reproducibility and forensic admissibility in legal
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proceedings. These advancements will further establish TGA as
an indispensable forensic tool, leading to more accurate, reliable
and safe analyses of explosive materials.
2.3 DSC in Forensic Analysis of Explosives
DSC plays a pivotal role in the forensic analysis of explosives,
offering crucial insights into thermal decomposition processes,
compatibility assessments and hazard evaluation. DSC operates
by measuring heat flow variations during phase transitions,
enabling forensic investigators to characterize the thermal stabil-
ity of explosives, detect exothermic reactions indicative of detona-
tion risks and assess interactions between energetic materials and
additives. Unlike traditional thermal techniques, DSC allows for
high-resolution thermokinetic profiling of explosives, identifying
unique thermal signatures critical for forensic classification
[25–28].
A notable application of DSC involves evaluating the chemical
compatibility and adhesion properties of energetic materials
with polymers and binders. This is particularly relevant in the
production of polymer-bonded explosives (PBX), where the ther-
mal interaction between an explosive and its binder influences
long-term stability and detonation characteristics. For instance,
DSC has been employed to study Royal Demolition Explosive
(RDX) and pentaerythritol tetranitrate (PETN) interactions with
polymer matrices, such as polystyrene (PS), nitrocellulose (NC)
and fluoroelastomers (FKM). Sample preparation involved finely
dispersing explosives within polymer binders followed by con-
trolled thermal cycling. Through DSC analysis, contact angle
measurements and vacuum stability tests, it was confirmed
that RDX exhibited greater adhesion compatibility than PETN,
reinforcing its suitability in PBX formulations [25]. Figure 5
presents DSC thermograms of RDX and PETN mixtures with
various polymeric binders, highlighting their thermal compati-
bility and decomposition behaviours. The observed peak shifts
indicate variations in adhesion and thermal stability, essential for
assessing binder suitability in PBX formulations.
Beyond compatibility studies, DSC has proven instrumental in
investigating the decomposition kinetics of novel high explo-
sives. Hu et al. [26] explored the thermal decomposition of
3,5-difluoro-2,4,6-trinitroanisole (DFTNAN) using DSC and TGA
across multiple heating rates. Their study quantified activation
energy and decomposition onset temperatures for DFTNAN-
RDX, DFTNAN-HMX and DFTNAN-TKX-50 binary mixtures,
identifying critical temperature thresholds at which detonation
risks increase. The research provides key safety insights for
explosive formulation, ensuring controlled detonation behaviour
and compatibility among high-energy materials [26].
Another critical forensic application of DSC is in assessing the
thermal stability of explosives mixed with contaminants. Sun
et al. [27] investigated the role of waste engine oil in emulsion
explosives, a common scenario in illicitly manufactured explosive
devices (IMEDs). DSC analysis revealed that the presence of
hydrocarbons lowered activation energy, accelerating decompo-
sition kinetics and increasing explosion hazards under improper
storage conditions. This study underscores the importance of DSC
in evaluating non-conventional explosive formulations, where
contaminants alter thermal properties in unpredictable ways
[27]. DSC has also been utilized in forensic fire and explosion
investigations. A study by Wolny et al. [28] demonstrated how
DSC and TGA helped reconstruct a fire-induced explosion sce-
nario involving AN and molten plastics. Their analysis revealed
that AN-polyethylene (PE) and AN-polypropylene (PP) mixtures
exhibited exothermic decomposition above 230◦C, leading to
the formation of ANFO-like compositions under fire conditions.
These findings provided forensic experts with valuable insight
into accidental explosive formation in industrial and agricultural
settings, guiding fire investigation protocols [28].
Despite its advantages, DSC-based forensic analysis presents sev-
eral challenges. The sensitivity of DSC signals can be influenced
by environmental factors, such as humidity, sample contam-
ination and heating rate variations, affecting reproducibility.
Additionally, DSC alone cannot provide direct mass loss data,
necessitating complementary TGA analysis for a complete foren-
sic profile. Emerging techniques such as high-resolution DSC
(HR-DSC) and modulated temperature DSC (MT-DSC) have
been developed to enhance thermal sensitivity, allowing for
sub-microgram sample analysis with improved resolution. To
further strengthen forensic applications of DSC, future research
should prioritize the integration of chemometric algorithms,
such as PCA and ML models, to enhance spectral interpretation
and improve forensic classification accuracy. Additionally, the
development of portable DSC devices is crucial for enabling
rapid on-site forensic examination, bridging the gap between
laboratory-based analysis and field investigations. Standardizing
forensic protocols for DSC analysis is equally important to
ensure reproducibility and the admissibility of results in forensic
investigations. These advancements will solidify DSC as an
indispensable forensic tool, allowing for more precise, reliable
and legally robust analyses of explosive materials.
2.4 GC–MS in Forensic Analysis of Explosives
GC–MS has become a cornerstone technique in forensic science,
offering unparalleled sensitivity and selectivity for the detection
and characterization of explosive materials. By separating volatile
and semi-volatile compounds through GC and subsequently
identifying them using MS, GC–MS enables forensic investi-
gators to obtain detailed molecular fingerprints of explosive
residues. This technique is particularly valuable for analysing
post-blast debris, environmental samples and complex forensic
matrices, where explosives may exist in trace concentrations or
be degraded by environmental factors. Advancements in sample
preparation methodologies, such as solid-phase microextraction
(SPME), headspace sampling and solvent extraction techniques,
have significantly improved the efficiency of forensic GC–MS
applications. Furthermore, derivatization strategies have been
increasingly employed to enhance the volatility and stability of
thermally labile compounds, ensuring optimal chromatographic
separation and detection [29–34].
Recent studies have demonstrated the adaptability of GC–MS
in forensic investigations, emphasizing its role in detecting a
broad range of explosive compounds with high specificity. Arce-
Rubí et al. [29] validated GC–MS and GC–NPD methods for the
forensic identification of explosive residues, showcasing their
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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).
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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.
ability to enhance the accuracy of post-blast investigations by
detecting a diverse array of organic explosive compounds. The
study employed an optimized solvent extraction protocol to
recover explosives from various forensic substrates, followed by
mass spectral deconvolution to resolve co-eluting species [29].
Similarly, Valdez and Leif [30] explored the utility of GC–MS
in analysing organophosphorus-based nerve agents (OPNAs),
demonstrating how derivatization reactions improve chromato-
graphic resolution and extend the detection range of these
hazardous compounds. This study underscored the need for
continued advancements in derivatization chemistry to maximize
the forensic applicability of GC–MS in nerve agent detection [30].
Advancements in trace explosive detection have also been
explored through specialized GC–MS inlet designs. Katilie et al.
[31] developed a method to detect vaporous ammonia, a key
marker of AN-based explosives, by leveraging in-inlet deriva-
tization to enhance the volatility of ammonium species. This
approach improved sensitivity and minimized sample handling,
addressing one of the critical challenges in forensic GC–MS appli-
cations [31]. Furthermore, the deployment of portable GC–MS
instruments has revolutionized forensic fieldwork, particularly
in military and counterterrorism operations. Leary et al. [32]
discussed the use of field-deployable GC–MS systems for real-
time identification of toxic chemical agents, highlighting their
role in rapid-response scenarios. The study demonstrated that
portable GC–MS instruments, while traditionally less sensitive
than laboratory-based systems, have reached a level of analytical
performance that allows for on-site forensic investigations with
minimal sample preparation [32]. Figure 6presents pictograms
used in portable GC–MS systems to guide operators in proper
sample introduction during field analysis. These visual aids
ensure procedural accuracy, particularly in high-pressure forensic
and military environments where precise sample handling is
critical for reliable toxic chemical detection.
Beyond environmental and field applications, GC–MS has been
employed in waterborne explosives analysis, providing forensic
experts with a powerful tool for monitoring contamination and
tracking the illicit use of explosive materials. Badjagbo and Sauvé
[33] demonstrated the effectiveness of GC–MS in analysing trace
explosive residues in water, emphasizing its role in environmental
forensics [33]. Similarly, forensic differentiation of explosive
formulations has been improved by integrating statistical analysis
with GC–MS datasets. Tsai et al. [34] employed multidimensional
GC–MS coupled with chemometric techniques to analyse and
differentiate plastic explosives based on their unique chemical
compositions. This approach allowed forensic analysts to estab-
lish chemical signatures for different formulations, enhancing the
traceability of explosive sources [34]. In a complementary study,
Suppajariyawat et al. [35] used a combined GC–MS and FTIR
approach, integrated with chemometric modelling, to classify
ANFO explosive samples based on their fuel compositions. Their
research highlighted the growing importance of multivariate
analysis in forensic science, demonstrating how statistical models
can improve the discrimination of explosive materials [35].
Figure 7presents PCA plots of ANFO supernatant samples,
illustrating their classification based on diesel composition. The
observed clustering highlights the effectiveness of chemometric
approaches in differentiating ANFO formulations, enhancing the
forensic traceability of explosive materials.
Despite its strengths, GC–MS faces challenges in forensic appli-
cations, particularly regarding the analysis of thermally labile
and non-volatile compounds. Although derivatization strategies
have improved the detectability of such compounds, they add
complexity to sample preparation and may introduce variability
if not carefully controlled. Furthermore, the presence of envi-
ronmental contaminants in forensic samples can lead to matrix
effects, necessitating rigorous cleanup procedures to prevent
analytical interferences. Another limitation is the inherent size
and operational constraints of portable GC–MS instruments,
which, despite recent advancements, still lag behind laboratory-
based systems in terms of resolution and sensitivity. Future
developments should focus on miniaturizing high-resolution
mass analysers for portable GC–MS, enhancing spectral decon-
volution algorithms to resolve complex forensic matrices and
integrating ML models for automated data interpretation. These
advancements will further establish GC–MS as an indispensable
tool in forensic investigations, allowing for more accurate, rapid
and reliable identification of explosive materials in a broad range
of forensic scenarios.
2.5 X-Ray Diffraction (XRD) in Forensic
Explosive Analysis
XRD is a non-destructive, high-precision analytical technique
that plays a vital role in forensic explosive analysis by charac-
terizing the crystalline structure, phase composition and purity
of explosive materials. By analysing the diffraction patterns
produced when x-rays interact with a crystalline substance, foren-
sic scientists can gain detailed insights into the identification,
structural transformations and phase transitions of explosives,
which are essential for forensic investigations and safety assess-
ments [36–39]. Recent advancements in high-resolution XRD
8of20 Analytical Science Advances,2025

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.
9of20

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.
techniques have significantly improved the accuracy of forensic
explosive characterization. Schachel et al. [36]demonstratedthe
integration of XRD into forensic substance analysis, incorporat-
ing it into a comprehensive forensic explosive database alongside
techniques such as high-performance liquid chromatography
(HPLC), HRMS and x-ray fluorescence (XRF). Their study
analysed pure explosive compounds and synthetic precursors,
preparing samples through grinding to uniform particle size
to enhance diffraction pattern consistency. This methodological
refinement significantly improved the detection of minor crys-
talline impurities, which is crucial for determining the purity,
origin and formulation history of explosive precursors. Such an
approach has enhanced the forensic discrimination of illicitly
manufactured explosives (IMEs) by providing unique structural
fingerprints that differentiate among synthesis pathways [36].
Beyond traditional applications, XRD has been explored for
analysing post-explosion residues to identify structural changes
resulting from detonation events. Qian et al. [37] used XRD to
analyse coal dust explosion residues, revealing distinct phase
transitions in quartz and aluminosilicate compounds. Their
research provided insights into the mineralogical transformations
occurring in post-explosion residues, offering valuable forensic
indicators for investigating coal mine explosions. The sample
preparation involved sieving, drying and moisture removal,
ensuring accurate mineral phase detection [37]. Li et al. [39]
further investigated the structural evolution of coal dust before
and after explosion events, using XRD to examine microstructural
changes in carbonaceous materials exposed to detonation condi-
tions. Their findings highlighted how shockwave-induced modi-
fications in crystallinity can be used to distinguish between pre-
and post-blast residues, thereby aiding in forensic reconstructions
of industrial explosion incidents [38]. Figure 8illustrates the
forensic application of XRD, highlighting structural phase tran-
sitions in post-explosion coal dust residues. The transformation
of carbonaceous materials, evident in the XRD patterns, serves as
a key forensic indicator for distinguishing pre- and post-explosion
samples, aiding in forensic explosion investigations.
In another notable application, Rajan et al. [38] utilized XRD to
characterize a new high-pressure phase of an energetic material,
demonstrating how shockwave-induced structural transforma-
tions influence the behaviour of explosives under extreme
conditions. This study correlated changes in XRD patterns and
Raman spectra, revealing crystallographic shifts that dictate the
energetic response of explosive materials. The processed samples
underwent controlled detonation simulations, mimicking real-
world forensic blast investigations to enhance our understanding
of phase transitions during explosive events [39].
Despite its advantages, XRD in forensic science faces several
limitations. The technique is highly effective for analysing pure,
well-crystallized samples, but its sensitivity decreases when deal-
ing with heterogeneous, amorphous or contaminated residues.
Sample preparation techniques, such as chemical washing, sep-
aration of amorphous debris and controlled drying, are often
necessary to improve signal clarity. Moreover, although XRD
provides detailed structural insights, it lacks chemical specificity,
often necessitating complementary methods such as GC–MS or
chromatography for comprehensive forensic analysis. Integrat-
ing XRD with chemometric approaches and multimodal spec-
troscopy could improve its reliability in forensic investigations
by enabling automated phase identification and differentiation of
complex explosive mixtures.
Future research should focus on enhancing XRD portability
for on-site forensic applications, improving its resolution for
mixed-phase explosive residues, and developing hybrid analytical
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TABLE 2 Comparative strengths and limitations of key analytical techniques for forensic analysis of explosives.
Technique Strengths Limitations
IR spectroscopy Non-destructive, high sensitivity to organic
explosives. ATR-FTIR enhances surface analysis,
and chemometric tools improve classification
accuracy. Portable versions enable on-site forensic
identification
Spectral overlaps in complex mixtures
reduce specificity. Limited effectiveness in
detecting inorganic explosives. Portable
devices lack high-resolution spectral
capabilities
TGA & DSC Crucial for thermal decomposition profiling,
explosion risk assessment and forensic
characterization of post-blast residues.
MEMS-TGA enables nanogram-scale analysis,
improving forensic sensitivity
Affected by environmental interferences
(humidity, contamination). Limited
portability for on-site forensic applications.
Requires careful sample preparation for
reproducibility
GC–MS High sensitivity for detecting trace explosive
residues in complex matrices. Portable GC–MS is
increasingly used in field-based forensic
applications. Capable of differentiating ANFO
and plastic explosives using chemometric models
Requires extensive sample preparation
(solvent extraction, derivatization). Portable
systems have 30%–50% lower resolution than
laboratory models. Non-volatile explosives
require complex modifications
XRD Provides non-destructive, high-resolution
crystallographic analysis of explosive precursors
and post-blast residues. Effective in linking
precursor materials to synthetic pathways. Useful
in forensic coal mine explosion investigations
Less effective for heterogeneous,
contaminated samples. Requires additional
methods (GC–MS, chromatography) for full
chemical specificity. Field applicability is
currently limited due to instrument size and
setup complexity
Abbreviations: ATR-FTIR, attenuated total reflectance Fourier-transform infrared spectroscopy; DSC, differential scanning calorimetry; GC–MS, gas
chromatography–mass spectrometry; IR, infrared; MEMS-TGA, micro-electromechanical systems-based thermogravimetric analysis; TGA, thermogravimetric
analysis; XRD, x-ray diffraction.
workflows that combine XRD with mass spectrometry and chro-
matography for a holistic forensic analysis. These advancements
will further establish XRD as a critical forensic tool for tracing
explosive precursors, investigating post-blast residues and ensur-
ing accurate forensic classifications of explosives. Table 2below
provides a comparative overview of the key analytical techniques
discussed in this review, highlighting their respective strengths
and limitations.
2.6 New Directions for Data Analysis and
Chemometrics in Forensic Explosive Analysis
In forensic science, the integration of chemometric techniques
has revolutionized data analysis, providing more accurate,
reliable and automated approaches for the classification and
interpretation of complex chemical datasets. Unlike traditional
qualitative assessments, modern chemometric algorithms enable
forensic scientists to extract meaningful patterns, identify rela-
tionships and classify explosive materials with greater precision.
The most commonly used multivariate statistical techniques
include PCA, LDA, soft independent modelling by class anal-
ogy (SIMCA) and cluster analysis. These methods enhance
the ability to distinguish between explosive substances and
environmental contaminants, a critical challenge in forensic
investigations. In recent years, data preprocessing techniques,
such as the Savitzky–Golay smoothing filter, standard normal
variate (SNV) transformation and wavelet transformation, have
been widely employed to improve spectral quality. These methods
correct baseline shifts, reduce noise and normalize spectral data,
thereby enhancing subsequent chemometric analyses. Advanced
chemometric strategies have also integrated ML models, such
as support vector machines (SVMs) and artificial neural net-
works (ANNs), to develop highly predictive forensic classification
models. Such methodologies have been particularly effective in
identifying trace amounts of explosives in post-blast residues and
complex environmental matrices, improving forensic detection
limits beyond conventional threshold values [16, 17, 40–45]. One
widely recognized software tool for chemometric analysis is
The Unscrambler, which has been extensively used in forensic
chemistry for multivariate statistical analysis of spectral data. The
software facilitates exploratory data analysis, clustering and pre-
dictive modelling, which are instrumental in forensic decision-
making. In addition to The Unscrambler, other platforms, such
as MATLAB, Python-based Scikit-learn and PLS_Toolbox, are
increasingly being adopted for forensic chemometric analysis,
particularly in developing automated detection algorithms for
explosives [2, 16, 17, 46–62].
2.7 Chemometrics in Explosive Analysis Using
IR Spectroscopy
The application of chemometric methods in IR spectroscopy has
significantly enhanced forensic explosive analysis, allowing for
more precise identification and classification of explosive materi-
als. Unlike conventional spectral interpretation, which relies on
qualitative assessments, chemometric algorithms facilitate robust
statistical modelling, improving both sensitivity and specificity
in detecting explosive residues. Spectral data obtained from IR
spectroscopy often consist of high-dimensional absorbance or
reflectance values, requiring sophisticated data processing for
11 of 20

meaningful forensic insights. These datasets can be derived from
spectroscopic imaging (pixel-based) or vibrational energy peak
tables, necessitating advanced chemometric approaches to filter
noise, correct baseline shifts and extract significant chemical
patterns for forensic decision-making [16, 17, 35, 57, 61]. A break-
through study by Risoluti et al. [57] demonstrated the potential
of portable NIR spectroscopy combined with chemometric mod-
elling for the on-site detection of explosive residues on human
skin. Their study employed a MicroNIR spectrometer to analyse
spectral data collected from 25 volunteers, optimizing a predictive
model capable of distinguishing explosive residues from environ-
mental contaminants. Preprocessing techniques, such as baseline
correction, normalization and noise reduction, were employed to
minimize individual skin variations, significantly improving clas-
sification accuracy. The study reported a classification accuracy of
over 90%, highlighting the reliability of chemometric techniques
in forensic explosive detection. However, a small sample size
and potential environmental variability limit the generalizabil-
ity of this model, necessitating further validation with diverse
population groups and environmental conditions [57].
To enhance the reliability of IR-based forensic detection, dimen-
sionality reduction techniques such as PCA have been extensively
utilized. PCA simplifies spectral datasets by extracting key
variance components, thereby improving interpretability while
reducing redundancy in high-dimensional data. Additionally,
classification models such as Partial Least Squares Regression
(PLSR) and Partial Least Squares Discriminant Analysis (PLS-
DA) have been employed to correlate spectral features with the
presence of explosives, mitigating challenges associated with
spectral collinearity and noise interference. The use of PLS-DA
enables forensic experts to differentiate explosives from non-
explosive residues based on chemometric modelling, providing a
statistically validated forensic framework for real-time explosive
detection [57].
Further advancements in quantum cascade laser (QCL)-assisted
IR spectroscopy have expanded forensic detection capabilities,
particularly for trace-level explosive analysis. Castro-Suarez et al.
[61] explored QCL-based mid-IR spectroscopy for detecting
explosive residues on diverse substrates, including suitcases
and environmental surfaces. Their study successfully identified
2,4,6-trinitrotoluene (TNT), PETN and RDX, along with volatile
precursors such as TATP and 2,4-dinitrotoluene (DNT). By
applying PCA and PLS-DA algorithms, the researchers effectively
distinguished spectral features of explosives from background
interference, significantly improving forensic classification accu-
racy. The ability to detect explosives at ultra-low concentrations
in real-world scenarios demonstrates the power of QCL-IRs in
forensic applications [61]. These advancements highlight the
critical role of chemometric methodologies in IR-based explosives
analysis. By integrating preprocessing, feature extraction and pre-
dictive modelling, forensic scientists can achieve more accurate,
reliable and real-time determinations of explosive materials, even
in highly contaminated or complex forensic samples. However,
challenges, such as spectral interferences, variability in envi-
ronmental conditions and limitations of field-portable devices,
remain areas for future research. Further optimization of ML-
enhanced chemometric models, along with standardized forensic
spectral databases, will enhance forensic detection accuracy,
reproducibility and real-world applicability.
2.8 Chemometric Methods in ATR-FTIR
Spectroscopy for Explosives Analysis
ATR-FTIR spectroscopy, when combined with chemometric
techniques, has become a powerful tool for forensic analysis,
particularly in the characterization of explosive materials. Recent
advancements in ATR-FTIR-based chemometric analysis have
enabled the precise classification of explosive residues by exploit-
ing their unique spectral fingerprints. By integrating data-driven
models, forensic experts can now distinguish among different
sources of explosive materials with improved accuracy. D’Uva
et al. [65] utilized ATR-FTIR spectroscopy alongside chemometric
methods to identify the source of AN in HMEs. This study applied
PCA and HCA to spectral datasets containing absorbance values
recorded at multiple wavenumbers. PCA effectively reduced
the dataset’s dimensionality by isolating spectral components
contributing the most variance, allowing forensic analysts to
differentiate between samples with distinct chemical signatures.
HCA was then applied to cluster these samples based on spectral
similarities, achieving a classification accuracy exceeding 90%.
These findings underscore the potential of ATR-FTIR chemo-
metric models for forensic material traceability, particularly in
distinguishing illicitly sourced AN from commercially available
counterparts [16].
Beyond AN classification, Banas et al. [17] explored multivariate
analytical techniques for post-blast residue analysis using ATR-
FTIR spectroscopy. Their study incorporated PLS-DA, a super-
vised classification technique widely used in spectral data inter-
pretation. The researchers applied preprocessing steps, including
baseline correction, noise reduction and spectral normalization,
to enhance signal clarity and reduce variability. Feature extraction
methods identified key spectral regions corresponding to spe-
cific functional groups in explosive compounds. The optimized
PLS-DA model demonstrated high sensitivity and specificity,
effectively distinguishing among different classes of explosive
residues in post-blast samples. This work highlights the efficacy
of chemometric-assisted ATR-FTIR in forensic investigations,
providing a robust and reproducible methodology for the rapid
identification and classification of explosives [17].
Despite these advancements, challenges remain in ATR-FTIR
forensic analysis, particularly when analysing complex post-
blast residues that contain environmental contaminants or over-
lapping spectral bands. The integration of more sophisticated
chemometric algorithms, such as ML-based classification mod-
els (e.g., SVMs and Random Forest classifiers), could further
enhance spectral differentiation and improve forensic classifi-
cation accuracy. Moreover, recent efforts have been made to
develop portable ATR-FTIR spectrometers equipped with real-
time chemometric processing, allowing for rapid on-site forensic
analysis without the need for extensive sample preparation.
Future research should focus on refining chemometric prepro-
cessing strategies to improve spectral clarity in forensic samples
affected by soot, hydrocarbons, or metallic residues. Additionally,
standardized chemometric modelling protocols across forensic
laboratories would enhance reproducibility and legal admissi-
bility, ensuring consistency in explosive material classification.
With continued advancements, ATR-FTIR spectroscopy inte-
grated with chemometric algorithms will remain a cornerstone
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in forensic explosive analysis, facilitating faster and more reliable
investigative outcomes.
2.9 Chemometrics in Explosive Analysis Using
Thermal Analysis Techniques
The integration of chemometric methods with thermal analysis
techniques, such as TGA and DSC, presents a significant advance-
ment in forensic investigations of explosive materials. These tech-
niques provide detailed insights into the thermal decomposition
behaviour, stability and kinetic parameters of explosives, which
are crucial for forensic classification and safety assessments.
The application of multivariate statistical methods enhances
the interpretability of thermal data, allowing for more accurate
discrimination of explosive materials based on decomposition
pathways, weight loss profiles and heat flow characteristics.
Although the direct forensic application of chemometrics in
the thermal analysis of explosives is still emerging, promising
methodologies can be adapted from related fields. Chauhan et al.
[63] demonstrated the integration of TGA with chemometric
analysis in soil sample examination, an approach that could
be extended to forensic explosive residue analysis. Their study
employed PCA and PLS-DA to extract meaningful thermal
degradation patterns from weight loss curves and derivative ther-
mogravimetry (DTG) profiles. By applying pattern recognition
techniques, this method facilitated the differentiation of soil
samples based on chemical composition and geographic origin
[63]. Similarly, in explosive forensic analysis, PCA and PLS-DA
can be used to classify post-blast residues based on their unique
thermal degradation pathways, offering an innovative method for
linking explosive materials to their sources.
Further advancements in chemometric-based thermal analy-
sis are highlighted by Mandal et al. [64], who assessed the
spontaneous combustion potential of coal samples using TGA
and DSC. Their study incorporated model-free and model-based
approaches to examine the kinetic parameters of thermal decom-
position. Baseline correction and smoothing techniques were
applied to improve data accuracy before chemometric modelling.
The isoconversional analysis method, a model-free technique,
proved particularly effective for determining activation energy
variations at different decomposition stages, eliminating the need
for predefined reaction models [64]. This adaptability makes it
a promising tool for forensic applications, as it allows for the
characterization of unknown or complex explosive compositions.
Additionally, model-based kinetic analysis segmented the ther-
mal data into multiple reaction steps, facilitating the precise
extraction of kinetic parameters, such as activation energy,
reaction order and frequency factor. These parameters provide
deeper insights into the thermal stability and safety profiles of
explosive residues, aiding forensic investigators in reconstructing
detonation scenarios and predicting hazardous conditions [64].
The fusion of chemometrics and thermal analysis has expanded
the forensic capability of TGA and DSC, enabling a more refined
classification of explosive residues. Advanced computational
techniques, such as ML algorithms and ANNs, are emerging
as powerful tools for enhancing the accuracy of thermal-based
chemometric models. Future research should focus on refin-
ing portable thermal analysis systems integrated with real-time
chemometric processing, thereby improving field-based forensic
capabilities. The standardization of chemometric methodologies
for forensic applications will further strengthen the reliability and
admissibility of thermal analysis in legal investigations.
2.10 Chemometrics in Explosive Analysis Using
GC–MS
The integration of chemometric techniques with GC–MS has
significantly enhanced the forensic analysis of explosives, par-
ticularly in the detection, classification and differentiation of
complex explosive formulations. GC–MS provides detailed chro-
matographic and spectral data, which chemometric approaches
utilize to detect patterns, classify unknown samples and improve
identification accuracy. Given the complexity of explosive
residues, multivariate data analysis methods, such as PCA, k-
nearest neighbours (k-NN) and LDA, have been employed to
extract meaningful chemical signatures and enhance forensic
interpretations [34, 35].
One notable study by Tsai et al. [34] demonstrated the effective-
ness of chemometric tools in analysing plastic explosives (PEs)
through comprehensive GC–MS profiling. Their research focused
on chromatographic peak alignment, which was used to correct
retention time shifts and improve consistency across samples.
PCA was then applied to the aligned chromatographic and mass
spectral data to identify key patterns, highlighting variations
among different production lots of PE. Classification was further
refined using the k-NN algorithm, which assigned unknown
samples to their respective production lots based on Euclidean
distance metrics. The study achieved a 0% classification error
rate, demonstrating the efficacy of chemometric models in distin-
guishing between closely related explosives with similar chemical
compositions. However, the requirement for extensive sample
preparation and reliance on controlled laboratory conditions pose
challenges for direct application in field scenarios, emphasizing
the need for robust, portable GC–MS systems integrated with
automated data preprocessing [34].
A significant advancement in this domain was reported by
Suppajariyawat et al. [35], who employed a combination of GC–
MS, FTIR and chemometric methods to classify ANFO samples
based on their fuel composition. Their study processed high-
dimensional chromatographic and spectral datasets, integrating
PCA and LDA for comprehensive forensic differentiation. PCA
played a pivotal role in identifying clustering patterns cor-
responding to variations in diesel fuel composition, enabling
effective discrimination between different ANFO formulations.
This approach facilitated the visualization of intrinsic sample
differences and streamlined forensic classification, reducing
analytical complexity [35]. Following PCA, LDA was applied
to maximize inter-group separation, correlating the identified
clusters to specific diesel brands and seasonal variations. The
model successfully classified ANFO samples with high accu-
racy, emphasizing the utility of chemometric-driven GC–MS
analysis in forensic investigations. Notably, preprocessing steps
such as normalization and variable transformation significantly
enhanced data quality, reducing experimental variability. The
findings of this study have substantial implications for forensic
science, particularly in tracing the origins of explosives and
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linking materials to manufacturers, thereby aiding criminal
investigations [35].
Despite these advancements, challenges persist in the appli-
cation of chemometric techniques in GC–MS-based explosive
analysis. Portable GC–MS instruments, while improving, still
struggle to match the sensitivity and resolution of their labo-
ratory counterparts. Additionally, complex sample matrices and
background interferences can impact classification accuracy,
necessitating advanced preprocessing techniques to improve
robustness. Future research should focus on refining automated
chemometric algorithms that can operate in real-time field
conditions, enhancing forensic capabilities for rapid and precise
explosive identification. Integrating ML approaches with GC–MS
chemometric analysis may further improve classification models,
offering a pathway towards more adaptive, high-throughput
forensic investigations of explosives.
2.11 Chemometrics in Explosive Analysis Using
X-Ray Techniques: XRD
The integration of chemometric techniques with XRD has
significantly enhanced the forensic characterization and discrim-
ination of explosive materials, particularly AN, which is widely
used in IEDs. XRD provides detailed structural information by
analysing diffraction patterns unique to each crystalline com-
pound, allowing forensic scientists to differentiate between pure
and adulterated forms of explosives. A study conducted by D’Uva
[65] demonstrated the efficacy of XRD in distinguishing among
various sources of AN used in explosives. Their research utilized
XRD to generate diffraction patterns that served as fingerprint
signatures for different AN formulations, revealing variations
in phase composition and structural properties influenced by
precursor materials and manufacturing processes [65].
To enhance forensic discrimination, chemometric techniques
such as PCA and LDA were applied to the XRD data. PCA
was instrumental in reducing data dimensionality while preserv-
ing critical variance, allowing for the visualization of inherent
groupings within the dataset. This approach facilitated the differ-
entiation among pure, commercial and homemade AN samples
based on their distinct crystallographic patterns. Following PCA,
LDA was employed to classify these samples into predefined cate-
gories, optimizing group separation and improving classification
accuracy [65].
Preprocessing of XRD data played a crucial role in improving ana-
lytical precision. Baseline correction and smoothing techniques
were applied to minimize background noise and enhance the
clarity of diffraction peaks. Normalization methods were also
used to standardize intensity values across different samples,
ensuring reliable comparisons. The combination of XRD and
chemometric modelling provided a robust methodology for foren-
sic material assignment, yielding highly accurate classification
results. Beyond AN analysis, XRD has broader forensic applica-
tions, including the identification of crystalline explosive residues
in post-blast investigations and the detection of adulterants in
explosive formulations. By leveraging ML algorithms alongside
chemometric techniques, forensic experts can automate pattern
recognition and enhance the sensitivity of explosive classification
models. However, challenges remain, particularly in analysing
contaminated samples or mixed-phase materials where overlap-
ping diffraction peaks may obscure forensic signatures. Future
advancements should focus on refining preprocessing strategies,
improving reference databases for forensic XRD pattern match-
ing, and integrating XRD with complementary spectroscopic
techniques such as Raman and FTIR for a more comprehensive
forensic analysis.
3Discussion and Critical Analysis
3.1 Effectiveness of Analytical Techniques
This review has critically examined the strengths and limitations
of various analytical techniques, including IR spectroscopy, GC–
MS, TGA/DSC and XRD, in the forensic analysis of HMEs. Each
method contributes uniquely to forensic investigations; how-
ever, their practical application, particularly in field conditions,
presents notable challenges. IR spectroscopy remains a valuable
technique for rapid, non-destructive molecular fingerprinting
of organic-based explosives, particularly in post-blast scenarios.
However, spectral complexity due to sample contamination and
overlapping bands can obscure identification accuracy. Although
preprocessing techniques such as baseline correction and nor-
malization mitigate some of these challenges, the sensitivity
of field-portable IR systems remains limited, requiring further
advancements in device robustness and chemometric integration.
GC–MS is widely recognized for its superior sensitivity and speci-
ficity in detecting volatile and semi-volatile explosive compounds.
Despite its effectiveness in controlled environments, its applica-
bility in field conditions is restricted due to the need for complex
sample preparation and derivatization for thermally unstable
compounds. Although portable GC–MS systems are emerging,
they have yet to match the precision and chromatographic
resolution of laboratory-based instruments. The integration of
automated sample preparation, enhanced chemometric data
processing and ML classification algorithms could improve its
usability in forensic investigations.
TGA and DSC provide crucial insights into thermal stability,
decomposition behaviour and kinetic profiles of explosives.
However, forensicapplications of these techniques remain under-
explored, with limited literature on their use for HMEs. The
challenges of environmental interferences, reproducibility con-
cerns and sample contamination further hinder their widespread
forensic adoption. Recent advances in portable TGA/DSC devices
and chemometric-assisted thermal data analysis offer promising
avenues for enhancing their forensic relevance. XRD plays a
key role in characterizing the crystalline structure of explosive
residues, which is valuable for identifying materials such as AN
and other precursor chemicals. Although XRD provides detailed
structural insights and preserves evidence integrity, its effec-
tiveness is reduced when dealing with contaminated or poorly
crystalline samples. To address these challenges, integrating XRD
with complementary techniques (e.g., GC–MS, IR) and ML-
driven phase pattern recognition could significantly enhance
forensic differentiation and classification accuracy. Overall, these
findings underscore the need for targeted research efforts to
bridge the gap between laboratory-based analytical precision
and real-world forensic applicability. By addressing challenges
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in portability, data complexity and sample preparation, forensic
science can enhance the real-time detection and classifica-
tion of HMEs, strengthening security measures and criminal
investigations.
3.2 Impact of Chemometric Methods
Chemometric techniques have significantly advanced forensic
analysis by improving the interpretation of complex analytical
datasets, particularly in the classification and identification
of explosive materials. Among the most commonly applied
techniques, PCA, LDA and PLS-DA have demonstrated signifi-
cant potential in forensic science. These multivariate statistical
methods reduce dimensionality, enhance classification accuracy
and extract meaningful patterns from high-dimensional forensic
datasets. However, their application in explosive analysis presents
both advantages and challenges that must be critically examined
to ensure their reliability in forensic investigations.
PCA is widely utilized for its ability to distil complex datasets,
such as spectral and chromatographic profiles, into principal
components that retain maximum variance. This technique is
particularly beneficial in IR, GC–MS and XRD-based foren-
sic analysis, where it allows forensic experts to distinguish
between explosive materials based on subtle chemical variations.
However, one critical limitation is that PCA operates as an unsu-
pervised technique, meaning that although it reveals patterns and
clusters, it does not inherently assign classifications to unknown
samples. Additionally, reducing dimensionality inevitably results
in the loss of some variance, which could affect the forensic
accuracy of distinguishing chemically similar explosives. This
trade-off between dimensionality reduction and classification
performance must be carefully balanced to maximize forensic
applicability.
LDA and PLS-DA, in contrast, are supervised classification
methods that utilize labelled training datasets to optimize sepa-
ration between predefined groups. LDA is particularly effective
when applied to datasets with well-defined class structures, such
as distinguishing different batches of AN-based explosives. It
maximizes the variance among classes while minimizing within-
class variance, leading to highly accurate forensic classification
models. However, LDA is sensitive to multicollinearity and
data noise, which can distort classification accuracy if spectral
or chromatographic data are not properly preprocessed. PLS-
DA, a regression-based technique, is often preferred for high-
dimensional datasets as it accounts for collinearity and spectral
overlapping, making it particularly useful in IR spectroscopy-
based forensic analysis. However, both LDA and PLS-DA require
large, well-curated training datasets, and their performance can
degrade when applied to real-world forensic samples that exhibit
environmental contamination, background noise or varying
chemical compositions.
One of the most critical challenges in forensic chemomet-
rics is data preprocessing, which significantly impacts clas-
sification accuracy and forensic reproducibility. Preprocessing
techniques, such as baseline correction, normalization, Savitzky–
Golay smoothing and peak alignment, are essential for improving
data quality before applying PCA, LDA or PLS-DA. For instance,
IR spectroscopy data require baseline correction to compensate
for instrument drift and environmental interference, whereas
GC–MS data require chromatographic peak alignment to correct
for retention time shifts across different sample runs. The effec-
tiveness of chemometric classification models depends heavily on
these preprocessing steps, and inconsistencies in data preparation
can lead to model instability and decreased forensic reliability.
Despite these advancements, the practical implementation of
chemometric techniques in real-time forensic workflows remains
limited. One major hurdle is the lack of standardized forensic
protocols for chemometric analysis, leading to variability in
model performance across different laboratories. Additionally,
portable forensic instruments lack the computational power to
execute complex chemometric algorithms in real-time, restricting
their field applicability. Recent advances in ML and AI-driven
chemometric models have shown promise in overcoming these
challenges. The integration of deep learning with PCA-based fea-
ture extraction and the development of automated chemometric
pipelines have the potential to improve classification accuracy
while reducing manual preprocessing requirements. However,
the forensic validation of these emerging techniques remains
an ongoing challenge. To enhance the forensic applicability of
chemometric approaches, future research should prioritize (1)
optimizing preprocessing pipelines to improve model robustness,
(2) integrating AI-driven chemometric models to enable real-time
forensic analysis and (3) standardizing chemometric method-
ologies across forensic laboratories to ensure cross-laboratory
reproducibility. Addressing these challenges will enhance the
accuracy, reliability and real-world applicability of chemometric
methods in forensic explosive analysis, ultimately strengthening
forensic science’s ability to detect and classify explosive materials
in high-stakes investigations.
Figure 9presents a detailed flow diagram that outlines the
key procedures involved in the forensic analysis of HMEs. The
workflow starts with the collection of diverse sample types,
including post-blast residues and precursor materials such as AN
and TATP. Field-specific challenges such as contamination and
the complexity of improvised components are addressed through
portable analytical methods, including NIR and portable GC–MS
systems. Laboratory analysis involves advanced techniques, such
as ATR-FTIR, GC–MS and XRD, to detect and classify explosive
signatures accurately. Chemometric tools like PCA, LDA and
PLS-DA play a critical role in managing complex datasets,
distinguishing among different formulations and enhancing
the reliability of classifications. These analyses culminate in
a comprehensive report, providing valuable investigative leads
by linking explosive materials to their sources and identifying
patterns in precursor usage.
3.3 Challenges and Gaps in Current Research
Despite significant advancements in forensic analysis of HMEs,
several challenges persist. One of the most pressing limitations
is the field usability of advanced analytical techniques such
as IR spectroscopy and GC–MS. Although these techniques
excel in laboratory environments, portable versions still struggle
with sensitivity, resolution and reproducibility when applied to
complex HME mixtures in real-world forensic investigations.
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FIGURE 9 Workflow for the forensic detection and analysis of homemade explosives (HMEs).
16 of 20 Analytical Science Advances,2025

TABLE 3 Challenges and future research directions in explosive material analysis.
Challenge
Technique
affected
Future research
direction Goal
Limited sensitivity in field
devices
IR, GC–MS Develop next-generation
miniaturized
spectrometers
Enhance real-time
forensic analysis in the
field
Complex mixtures &
contamination effects
IR, XRD, TGA/DSC Improve spectral
preprocessing &
chemometric correction
Increase accuracy in
detecting mixed residues
Underutilization of
thermal analysis
TGA, DSC Expand research on
thermal properties of
HMEs
Strengthen post-blast
investigation
methodologies
Challenges in field
chemometric integration
PCA, LDA, PLS-DA Embed AI-driven
chemometrics in portable
systems
Automate classification &
reduce human variability
Abbreviations: AI, artificial intelligence; DSC, differential scanning calorimetry; GC–MS, gas chromatography–mass spectrometry; HMEs, homemade explosives;
IR, infrared; LDA,linear discriminant analysis; PCA, principal component analysis; PLS-DA, Partial Least Squares Discriminant Analysis; TGA, thermogravimetric
analysis; XRD, x-ray diffraction.
The chemical complexity of HMEs also poses a substantial
challenge, as they often contain heterogeneous components and
environmental contaminants, complicating accurate detection
and classification. This issue is particularly problematic in post-
blast scenarios, where explosive residues may be mixed with
debris, soil or biological materials, necessitating advanced sample
preprocessing and improved chemometric approaches.
Another critical gap in current research is the underutilization
of thermal analysis techniques such as TGA and DSC in HME
studies. Although these methods have demonstrated value in
evaluating the thermal stability and decomposition pathways
of conventional explosives, their forensic application to HMEs
remains largely unexplored. Future research should focus on
adapting TGA and DSC for field-based explosive residue analysis,
particularly by integrating them with chemometric algorithms to
enhance classification accuracy. Additionally, although chemo-
metric techniques such as PCA and PLS-DA have been widely
used in controlled laboratory settings, their integration into real-
time portable forensic tools remains limited. To address this, auto-
mated, adaptive chemometric algorithms capable of handling
environmental variability must be developed to facilitate rapid
on-site forensic investigations. Overcoming these challenges will
be crucial in advancing forensic capabilities for the detection,
identification and classification of HMEs.
4 Conclusion
The increasing threat posed by IEDs, particularly those involving
HMEs, underscores the need for advanced, rapid and reliable
forensic analysis techniques. This review has examined key
analytical approaches, including IR spectroscopy, GC–MS, TGA,
DSC and XRD, that provide critical molecular, structural and
thermal data essential for explosive identification and classifica-
tion. The integration of chemometric techniques, such as PCA,
LDA and PLS-DA, has further enhanced the accuracy and inter-
pretability of forensic data, allowing for the effective classification
of explosives even in complex, contaminated forensic samples.
Despite these advancements, challenges persist in adapting these
technologies for real-time, field-based forensic applications. The
sensitivity and resolution of portable instruments remain lim-
ited compared to laboratory-based counterparts, restricting their
effectiveness in on-site forensic investigations. Additionally, the
forensic application of thermal analysis techniques (TGA and
DSC) remains underexplored, particularly for the characteriza-
tion of post-blast residues and the thermal stability of HMEs.
Furthermore, the lack of standardized chemometric protocols
across forensic laboratories introduces variability in data inter-
pretation, necessitating the development of unified analytical
workflows. Future research should prioritize the advancement
of portable forensic tools, the forensic application of thermal
analysis techniques and the standardization of chemometric
protocols. Strengthening these areas will enhance forensic sci-
ence’s ability to detect, classify and trace explosives with greater
accuracy, reinforcing forensic intelligence capabilities in criminal
investigations and global security.
5Future Directions
To address the current challenges in forensic explosive analysis,
future research should focus on enhancing portable analytical
technologies, expanding forensic applications of thermal anal-
ysis, standardizing chemometric methodologies and promoting
interdisciplinary collaboration. Portable IR and GC–MS systems
require improved sensitivity and resolution to match laboratory-
based instruments, enabling real-time forensic detection in field
conditions. The integration of advanced chemometric algorithms,
such as ML-enhanced PCA, LDA and PLS-DA, could signifi-
cantly improve classification accuracy and forensic reliability,
particularly when dealing with complex explosive residues.
Further research should expand the application of TGA and DSC
techniques for HMEs, as the thermal properties of these materials
remain underexplored in forensic science. A deeper understand-
ing of thermal degradation pathways will improve post-blast
residue analysis, aiding in tracing explosive compositions and
17 of 20

predicting detonation risks. Standardizing chemometric applica-
tions is another critical priority, as variations in preprocessing
techniques, statistical modelling and spectral analysis across
forensic laboratories currently hinder cross-study reproducibil-
ity. Establishing unified analytical protocols will enhance the
consistency and accuracy of forensic explosive classification.
Interdisciplinary collaboration among analytical chemists, foren-
sic scientists and statisticians will drive the development of
more robust and adaptable forensic methodologies. By fostering
research that bridges the gap among analytical chemistry, data
science and forensic applications, forensic science can move
towards more precise, legally admissible and intelligence-driven
methods for explosive detection and source attribution. Address-
ing these future research priorities will ensure that forensic
science remains at the forefront of security and criminal investi-
gations, ultimately strengthening global safety measures against
illicit explosive threats. A summary of key challenges in forensic
explosive analysis, along with corresponding affected techniques
and future research directions, is presented in Table 3.
Author Contributions
Abdulrahman Aljanaahi: original draft’s conceptualization, method-
ology, data curation, investigation, writing. Muhammad Kamran
Hakeem: conceptualization, formal analysis, writing review and editing.
Abdulla Aljanaahi: revision and editing. Iltaf Shah: supervision,
funding acquisition, project administration, resources, validation, visual-
ization, review and editing.
Acknowledgements
Open access publishing facilitated by United Arab Emirates University,as
part of the Wiley - United Arab Emirates University agreement.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability Statement
The data that support the findings of this study are available from the
corresponding author upon reasonable request.
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