Decoding the Complexity of Homemade Explosives: Forensic Insights from 344 Chinese Case Samples
Organized by: International Forensic Scientist Awards
Website: forensicscientist.org
15th Edition of Forensic Scientist Awards 27-28 October 2025 | Paris, France
Homemade explosives (HMEs) remain one of the most persistent challenges in forensic science. Unlike conventional explosives, HMEs are created using a wide range of chemicals and improvised construction methods, making them difficult to trace and analyze. Forensic investigators often struggle to determine the origin of these materials — a crucial step in linking evidence across different crime scenes.
To shed light on this issue, our team analyzed 344 HME samples from 129 real criminal cases in China, collected between 2015 and 2022. Each sample was first categorized according to its role — whether it was a main charge, initiator, or precursor/other material — and examined using standard forensic procedures. But given the sheer complexity of these mixtures, we went a step further.
We applied Association Rule Mining (ARM), a powerful data-mining technique commonly used in fields like market basket analysis. In this forensic context, ARM helped us identify hidden patterns in chemical combinations that human experts might easily overlook.
What We Found
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Common “recipes” used by bomb-makers in China, based on frequently co-occurring components.
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New forensic signatures that can help investigators link separate cases together.
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Predictive patterns, where even a partial explosive sample could suggest the likely ingredients and construction method.
Why It Matters
This is the most comprehensive forensic analysis of HMEs in China to date, offering both practical and scientific value. By combining traditional forensic methods with advanced data mining, we provide investigators with stronger tools to:
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Trace the origins of homemade explosives,
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Connect seemingly unrelated cases, and
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Strengthen global efforts to combat terrorism and criminal use of HMEs.
Looking Ahead
Our findings highlight the importance of data-driven forensic science. By leveraging machine learning and big data techniques, we can push beyond traditional limitations and uncover insights that help protect public safety worldwide.
🔗 Learn more and apply at:
https://forensicscientist.org/
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