Scholarlink

<p>Smokeless powder is the primary propellant in civilian and military ammunition, and in China, the use of propellants to make homemade ammunition and bombs is an emerging criminal practice. The identification and differentiation of the propellants used can provide forensic information about their sources. Depending upon the ammunition manufacturer and type, the recipe of propellants varies, and the characterization of smokeless powders in terms of their spectral components is useful for differentiating propellants. In this work, near-infrared spectroscopy (NIR) and chemometric modeling were used to explore the feasibility of differentiating and predicting smokeless powders from different sources. By comparison, the proposed neural network model in the study exhibited an average accuracy of over 80%. Furthermore, the potential for differentiating smokeless powders was well demonstrated via simple and rapid near-infrared spectroscopic analysis, and the employment of chemical agents and time-consuming chromatography and mass spectrometry could thereby be avoided.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A study for potential rapid discrimination of smokeless powders by near-infrared spectroscopy and chemometric modeling methods for forensic application

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月3日

查看全文
  • Hongling Guo,
  • Haoyuan Shi,
  • Yinghua Feng,
  • Yiting Guo,
  • Xiuli Zhang,
  • Ping Wang,
  • Can Hu,
  • Hongcheng Mei,
  • Yajun Li,
  • Jun Zhu

摘要

Smokeless powder is the primary propellant in civilian and military ammunition, and in China, the use of propellants to make homemade ammunition and bombs is an emerging criminal practice. The identification and differentiation of the propellants used can provide forensic information about their sources. Depending upon the ammunition manufacturer and type, the recipe of propellants varies, and the characterization of smokeless powders in terms of their spectral components is useful for differentiating propellants. In this work, near-infrared spectroscopy (NIR) and chemometric modeling were used to explore the feasibility of differentiating and predicting smokeless powders from different sources. By comparison, the proposed neural network model in the study exhibited an average accuracy of over 80%. Furthermore, the potential for differentiating smokeless powders was well demonstrated via simple and rapid near-infrared spectroscopic analysis, and the employment of chemical agents and time-consuming chromatography and mass spectrometry could thereby be avoided.