Asset Details
Do you wish to reserve the book?
Improving first responder forensic capabilities: On-site detection and quantification of explosive precursors using portable near-infrared spectroscopy and machine learning
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Improving first responder forensic capabilities: On-site detection and quantification of explosive precursors using portable near-infrared spectroscopy and machine learning
Do you wish to request the book?
Improving first responder forensic capabilities: On-site detection and quantification of explosive precursors using portable near-infrared spectroscopy and machine learning
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Improving first responder forensic capabilities: On-site detection and quantification of explosive precursors using portable near-infrared spectroscopy and machine learning
2025
Overview
In this study, we assess the effectiveness of portable near-infrared (NIR) spectroscopy coupled with advanced machine learning algorithms for on-site detection and quantification of key explosive precursors, in accordance with EU Regulation 2019/1148. The research focuses on developing robust quantitative models for hydrogen peroxide, nitromethane, and nitric acid, addressing the challenge of varied concentrations and compositions encountered by first responders. The models demonstrated high predictive accuracy, with Root Mean Square Error of Prediction (RMSEP) values of 0.96 % for hydrogen peroxide, 2.46 % for nitromethane, and 0.70 % for nitric acid across diverse samples. The qualitative models created for those explosives precursors also showed high effectiveness and reliability, with minimal false negatives and false positives. The integration of machine learning algorithms facilitated the adaptation of these models to handle the complex variability of precursor formulations effectively. Additionally, the utilization of cloud operating systems provided significant advantages for real-time analysis and continuous data updating, essential for maintaining the accuracy and relevance of the models in rapidly changing field conditions. This research highlights the potential of integrating advanced spectroscopic techniques and machine learning within a cloud-based framework to improve the detection and management of explosive precursors in field settings. This integration enables the reliable detection and quantification of these precursors in a matter of seconds. Future work will extend this approach to additional precursors and explore complementary technologies to further enhance on-site detection capabilities.
•Portable NIR spectroscopy detects and quantifies explosive precursors.•Machine learning models capture the variability of precursor formulations.•NIR architectures ensures accurate on-site detection and legal compliance.•Cloud-based systems enable real-time updates and decentralized analysis.
