从质谱检测芬太尼类型的新型深卷积网络
Zhenchuang Wang1, Ping Xu1, Yang Zhao1
1College of Automation, Hangzhou Dianzi University, Hangzhou, China.
Journal of mass spectrometry : JMS
|August 22, 2025
概括
一个新的深卷积网络 (DSCN) 准确地从质谱中检测到芬太尼类似物. 这种先进的模型改进了现有的识别这些危险物质的方法,
科学领域:
- 分析化学
- 法医科学
- 计算化学
背景情况:
- 与芬太尼和类似物滥用相关的死亡率上升需要改进检测方法.
- 由于易于合成和快速出现,现有的分类模型难以准确识别多种芬太尼类型.
- 从电子冲击 (EI) 或电喷离子 (ESI) 质谱中检测芬太尼类型的小样本是一个重大挑战.
研究的目的:
- 提出一个新的深卷积网络 (DSCN) 准确分类和检测芬太尼类型.
- 解决当前机器学习和深度学习模型在从质谱数据中识别芬太尼类型的局限性.
- 提高芬太尼类型的检测能力,特别是在样本规模有限的场景中.
主要方法:
- 增加质谱为语网络创建输入光谱对.
- 在语架构中使用1D卷积神经网络 (CNN) 进行有效的光谱特征提取.
- 采用完全连接 (FC) 和Softmax层的分类网络进行最终模拟检测.
- 结合对比损失和交叉损失函数用于DSCN参数训练.
主要成果:
- 与其他已知的机器学习和深度学习技术相比,拟议的DSCN模型在检测芬太尼类型方面表现出更好的表现.
- DSCN有效地提取相关的光谱特征来区分芬太尼类似物.
- 该模型在涉及小样本质谱的分类任务中获得了更高的准确性.
结论:
- 开发的DSCN为准确检测芬太尼类似物提供了有希望和有效的解决方案.
- 这种深度学习方法显著提高了基于质谱分析识别非法物质的能力.
- 该模型有可能在法医科学和旨在应对阿片类药物危机的公共卫生倡议中应用.
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