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深度学习模型在多个拉曼光谱仪之间通过低级适应进行校准转移
Jiahui Lai1, Miaomiao Li1, Song Chen1
1College of Chemistry and Chemical Engineering, Central South University, Hunan, Changsha 410083, China.
Analytical chemistry
|September 9, 2025
概括
一种新的基于低级适应的校准转移 (LoRA-CT) 方法使得拉曼光谱仪之间的高效深度学习模型转移成为可能. 这种方法显著提高了采用最小样本的准确性,克服了便携式光谱的设备间变异.
科学领域:
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
- 机器学习 机器学习
背景情况:
- 拉曼光谱中的深度学习允许快速分析,但由于设备间的变化,模型可移植性面临挑战.
- 频谱仪之间的系统差异阻碍了训练模型在不同仪器中直接应用.
研究的目的:
- 在基于深度学习的拉曼光谱学中引入一个参数有效的校准转移方法.
- 以最小的数据和计算资源,在各种频谱仪上实现无的模型可移植性.
主要方法:
- 提出了一种基于低级适应的校准转移 (LoRA-CT) 方法,用于参数高效的微调.
- 分解重量更新为低级矩阵,以显著减少可训练参数 (600×减小与完整微调).
- 在三个数据集中验证了LoRA-CT,包括溶剂混合物和混合油.
主要成果:
- 与传统方法相比,LoRA-CT使用很少的转移样本显示出优异的校准转移性能.
- 在甲醇混合物试验组中实现了R2 = 0.952和RMSE = 0.072,超过了分片直接标准化和完整参数微调的性能.
- 实验结果证实了准确性和效率的显著改善.
结论:
- LoRA-CT为资源效率高的光谱模型部署建立了一个新的范式.
- 模块化,插即用设计可促进光谱仪之间的动态切换.
- 这种方法对便携式光谱仪和面临样本稀缺和计算限制的多仪器系统特别有利.
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