现代仪器与医疗

激光诱导击穿光谱结合神经网络模型在生物医学领域的应用研究进展

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马越 , 杨光 , 陈建军 ^*

现代仪器与医疗 | 综述 2026,32(2): 104-111

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现代仪器与医疗 |综述 2026 , 32 (2) : 104 -111

激光诱导击穿光谱结合神经网络模型在生物医学领域的应用研究进展

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马越, 杨光, 陈建军^*

作者信息

新疆医科大学医学工程技术学院，乌鲁木齐　830000

通讯作者:

陈建军，男，博士研究生，教授，研究方向：生物信息处理

作者简介:

马越，男，硕士研究生，研究方向：生物信息处理

Research progress on the application of laser-induced breakdown spectroscopy combined with neural network model in the biomedical field

Yue Ma, Guang Yang, Jianjun Chen^*

Affiliations

School of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830000, China

出版时间: 2026-04-10 doi: 10.11876/mimt202602017

文章导航

摘要

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激光诱导击穿光谱技术（laser-induced breakdown spectroscopy，LIBS）因其快速、原位、微损及多元素同步分析的优势，在生物医学检测领域受到广泛关注。然而，其固有的光谱噪声、基质效应等问题制约了分析精度。近年来，神经网络模型强大的非线性拟合与特征提取能力为克服这些瓶颈提供了有效的解决方案。本文系统综述了LIBS技术与神经网络模型相结合在生物医学领域的应用研究进展，重点探讨了该联合策略在在肿瘤诊断与分类、癌症分期与治疗监测、细菌与病原体识别以及中药分析与溯源等方面的关键研究成果。研究表明，通过结合人工神经网络、卷积神经网络、脉冲神经网络等模型，能够从复杂的LIBS光谱中提取深层特征，显著提升分类与定量分析的准确性与鲁棒性。LIBS与神经网络的融合形成了“快速检测+智能解析”的协同效应，展现出巨大的临床应用潜力，为未来开发智能化、高精度的即时诊断系统奠定了坚实的技术基础。

关键词

激光诱导击穿光谱 / 神经网络 / 生物医学检测 / 元素分析

Abstract

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Laser induced breakdown spectroscopy (LIBS) has attracted extensive attention in the field of biomedical detection due to its advantages of rapid, in situ, micro-damage and multi-element synchronous analysis. However, its inherent spectral noise, matrix effect and other problems restrict the analysis accuracy. In recent years, the powerful nonlinear fitting and feature extraction capabilities of neural network models provide effective solutions to overcome these bottlenecks. This paper systematically reviews the research progress of LIBS technology combined with neural network model in biomedical field, focusing on the key research achievements of this combined strategy in tumor diagnosis and classification, cancer staging and treatment monitoring, bacterial and pathogen identification, as well as traditional Chinese medicine analysis and traceability. Studies have shown that by combining artificial neural network, convolutional neural network, spiking neural network and other models, it can extract deep features from complex LIBS spectra, significantly improve the accuracy and robustness of classification and quantitative analysis. It is concluded that the integration of LIBS and neural network forms a synergistic effect of “rapid detection+intelligent analysis”, which shows great clinical application potential and lays a solid technical foundation for the development of intelligent, high-precision point-of-care diagnosis system in the future.

Key words

Laser-induced breakdown spectroscopy / Neural network / Biomedical testing / Elemental analysis

引用本文

马越, 杨光, 陈建军. 激光诱导击穿光谱结合神经网络模型在生物医学领域的应用研究进展. 现代仪器与医疗, 2026 , 32 (2) : 104 -111 . DOI: 10.11876/mimt202602017

Yue Ma, Guang Yang, Jianjun Chen. Research progress on the application of laser-induced breakdown spectroscopy combined with neural network model in the biomedical field[J]. Modern Instruments & Medical Treatment, 2026 , 32 (2) : 104 -111 . DOI: 10.11876/mimt202602017

基金

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国家自然科学基金项目(31760269)
新疆维吾尔自治区“天山青年”优秀人才项目(2018Q021)

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2026年第32卷第2期

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文章信息

doi: 10.11876/mimt202602017

首发时间：2026-10-23
出版时间：2026-04-10

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出版历史

基金

国家自然科学基金项目(31760269)

新疆维吾尔自治区“天山青年”优秀人才项目(2018Q021)

作者信息

新疆医科大学医学工程技术学院，乌鲁木齐　830000

通讯作者:

陈建军，男，博士研究生，教授，研究方向：生物信息处理

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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