神经网络指导全球最小方程用于拉曼光谱中的定量生物化学成分分析
Zelin Peng1,2,3,4, Junjiang Liu1,2,3,4, Linyuan Zhao1,2,3,4
1Institute of Electromagnetics and Acoustics, School of Electronic Science and Engineering, Xiamen University, Xiamen 361100, China.
Analytical chemistry
|January 9, 2026
概括
一个新的混合算法,神经网络引导的全球修改最小方形 (NN-GMLS),增强了拉曼光谱分析. 这种方法改善了定量生物化学分析,特别是有限的数据,并有助于白血病细胞分类.
科学领域:
- 分析化学 分析化学
- 生物化学 生物化学
- 频谱学是一种光谱学.
背景情况:
- 拉曼光谱是一种无标签的技术,在制药,生物学和食品分析中具有价值.
- 最小平方回归和神经网络对于光谱分析很常见,但在许多参数或小数据集方面存在局限性.
- 拉曼光谱中的定量生物化学成分分析面临着与收和有限的训练数据的挑战.
研究的目的:
- 提出一种混合算法 (NN-GMLS),解决拉曼光谱分析现有方法的局限性.
- 增强拉曼光谱中的定量生物化学成分分析,特别是对于小或可变数据集.
- 用拉曼光谱来提高白血病细胞状态的分类精度.
主要方法:
- 开发了一种混合算法 (NN-GMLS),结合了神经网络和全球修改最小平方.
- 利用经过修改的实验光谱的模拟数据进行神经网络训练.
- 在表面增强拉曼光谱 (SERS) 和自发拉曼光谱数据集上验证了NN-GMLS方法.
主要成果:
- 与K562白血病细胞数据的独立方法相比,NN-GMLS在定量生化分析中表现出更高的准确性.
- 该方法即使在参考光谱的潜在变化或不准确的情况下也表现出有效性.
- 在NN-GMLS合成数据上训练的1D ResNet-10在分类白血病细胞状态方面实现了87.5%的准确性.
结论:
- 混合NN-GMLS算法为拉曼光谱中的定量生物化学分析提供了更高的准确性.
- 这种方法在训练数据有限的场景中特别有用,例如微生物和细胞样本分析.
- 对NN-GMLS来说,它对推进光谱分析和细胞分类应用有很大的前景.
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