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促进活体菌的鉴定:Kolmogorov-Arnold网络指导的卷积神经网络与激光笔相结合,拉曼光谱学

Yifan Sun¹, Xiao Peng¹, Fusheng Du²

¹State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), College of Physics and Optoelectronic Engineering, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen, Guangdong 518060, China.

|November 25, 2025

概括

这项研究介绍了一种先进的平台,用于识别使用激光 tweezers 拉曼光谱 (LTRS) 和适应性科尔莫戈罗夫-阿诺德网络 (KAN) 引导的卷积神经网络 (CNN) 的细菌子. 这种方法可以准确识别细菌子,提高食品安全和人类健康.

科学领域:

微生物学微生物学
频谱学是一种光谱学.
人工智能的人工智能

背景情况:

细菌子是食物传播和动物传播疾病的重要载体.
准确识别细菌子对于食品安全和公共卫生至关重要.
现有的方法可能面临着小数据集和不可培养的微生物的挑战.

研究的目的:

开发一种用于精确识别活生生的细菌子的新平台.
通过机器学习和光谱学来提高子鉴定的准确性和稳定性.
为了研究导致Bacillus子子分类的关键光谱特征.

主要方法:

激光 tweezers 拉曼光谱 (LTRS) 与适应性科尔摩戈罗夫-阿诺德网络 (KAN) 引导卷积神经网络 (CNN) 的集成.
利用基于高斯噪声的光谱增强来扩大和丰富小单细胞拉曼光谱数据集.
使用阻断个体拉曼波段方法来确定特定拉曼波段对分类的贡献.

主要成果:

通过使用KAN引导的CNN,对五种Bacillus子物种实现了97.80%±1.79%的预测准确度.
在独立的光谱数据集上表现出强大的稳定性和概括能力,准确度为96%.
确定了1655厘米-1的拉曼波段 (蛋白胺I) 作为对分类的主要贡献者,表现优于Ca-DPA波段.

结论:

由KAN引导的CNN与LTRS相结合,提供了一个强大而准确的平台来识别细菌子.
开发的方法对微生物识别,特别是对不能培养的微生物有很大的前景.
这种方法有助于推进食品安全和了解微生物病原性.

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