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通过比较来自八种不同的光谱仪的拉曼光谱上的机器学习方法

Christoph Lange¹, Maxim Borisyak¹, Martin Kögler²

¹Technische Universität Berlin, Faculty III Process Sciences, Institute of Biotechnology, Chair of Bioprocess Engineering, Straße des 17. Juni 135, Berlin, 10623, Berlin, Germany.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|February 25, 2025

概括

从多个光谱仪获得的拉曼光谱数据上训练的卷积神经网络 (CNN) 优于传统的部分最小平方 (PLS) 模型. 这种方法简化了生物技术实验室的校准,并提高了测量关键分析物的准确性.

科学领域:

生物技术是生物技术.
分析化学分析化学
化学测量化学测量化学测量
机器学习机器学习

背景情况:

拉曼光谱是一种有价值的过程分析技术 (PAT),用于生物技术中的非侵入性分子分析.
机器学习模型对于将复杂的光谱数据转化为可量化的度至关重要.
部分最小平方 (PLS) 模型,假设线性关系,通常使用,但在复杂的生物系统中可能是有限的.

研究的目的:

为了评估单个卷积神经网络 (CNN) 在多个拉曼光谱仪的数据上训练的性能.
为了比较CNN与传统PLS模型的有效性,用于生物技术中的定量分析.
评估一个统一的CNN模型的潜力,以简化跨多种光谱数据集的校准.

主要方法:

用已知度的葡萄糖,酸和硫酸准备样本.
从这些样本中采集了超过2200个拉曼光谱,使用了八种不同的光谱仪.
在所有八个光谱仪的综合光谱数据上训练单个CNN模型.
将CNN模型的性能与传统PLS模型进行比较.

主要成果:

在所有八个光谱仪数据上训练的单个CNN模型显著优于单个PLS模型.
联合CNN方法证明了多个光谱仪的实验室的整体精度提高和校准工作减少.
分析发现三个特定的光谱仪更适合准确量化葡萄糖,酸和硫酸.

结论:

统一的CNN模型为生物技术中的拉曼光谱提供了一个强大而高效的替代方案,而不是传统的PLS校准.
这种多光谱仪CNN方法提高了准确性并简化了工作流程,对于使用多种仪器的实验室来说尤其有益.
这些发现突出了先进机器学习在复杂的分析挑战中克服传统方法的局限性的潜力.

关键词:

卷积神经网络是一种卷积神经网络.机器学习机器学习部分最小平方的最小平方.拉曼光谱法拉曼光谱法拉曼光谱法

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