开放式深度学习启用了LIBS传感器,用于无标签和现场识别未知病原体粉末
Shutong Liu1, Yibo Wang1, Zixiao Wang1
1School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, China.
Talanta
|February 19, 2026
概括
一种新的深度学习方法增强了激光诱导分解光谱 (LIBS) 用于识别病原体粉末. 这种先进的技术可以准确检测已知的病原体,同时有效拒绝未知的物质,提高生物安全性.
科学领域:
- 光谱学和分析化学的研究.
- 人工智能和机器学习
- 生物安全和公共卫生.
背景情况:
- 在现场,无标签识别病原体粉末对于生物安全和生物恐怖主义应对至关重要.
- 激光诱导分解光谱 (LIBS) 和深度学习显示出对粉末分析的前景.
- 当前的深度学习模型难以拒绝未知的样本类型,从而限制了现实世界的可靠性.
研究的目的:
- 开发一种新的LIBS传感技术,使用开放式深度学习来可靠地识别病原体粉末.
- 提高深度学习模型在实时区分已知和未知样本的能力.
- 为了提高生物安全应用的病原体检测系统的准确性和稳定性.
主要方法:
- 通过将分类重构开放集识别 (CROSR) 策略与剩余网络 (ResNet) 集成,开发了一种新的开放集深度学习模型.
- 使用LIBS获取病原体粉末的光谱数据.
- 在一个包括已知的目标病原体和各种未知样本的测试集上评估了模型的性能.
主要成果:
- 使用CROSR的ResNet模型实现了已知的病原体类别的高分类准确性.
- 该模型在拒绝未知样品方面表现出显著的改进,对九种类型的未知物质的准确率为86.6%.
- 与OpenMax策略相比,拟议的模型提高了目标病原体识别精度11.7%,未知类排斥11.9%.
结论:
- 由开放式深度学习驱动的LIBS传感技术为现场,无标签的病原体粉末识别提供了一个有前途的解决方案.
- 该CROSR策略显著提高了深度学习模型在区分已知的病原体和未知的材料的可靠性.
- 这种方法对提高生物安全和快速反应能力具有广泛的应用前景.
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