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拉曼光谱-机器学习集成:在模拟墙面表面白色颜料中推进Na2SO4和CaCO的高精度定量分析

Chenjia Song¹, Rongling Zhang¹, Qian Zhou¹

¹Key Laboratory of Synthetic and Natural Functional Molecular Chemistry of Ministry of Education, College of Chemistry & Material Science, Northwest University, Xi'an 710127, China.

Analytical chemistry

|December 3, 2025

概括

拉曼光谱和部分最小方形 (PLS) 在模拟壁画中准确量化硫酸和碳酸. 这种方法增强了对文化遗产保护的分析和了解盐引起的恶化.

科学领域:

分析化学分析化学
材料科学材料科学材料科学
艺术保护科学艺术保护科学

背景情况:

古老的壁画面临着环境因素和人类活动的退化.
由盐引起的损坏,包括脱皮和裂,损害了壁画的完整性和艺术价值.
准确量化盐度对于有效的保护策略至关重要.

研究的目的:

在模拟壁画样本中开发和验证硫酸 (Na2SO4) 和碳酸 (CaCO3) 的定量分析方法.
评估拉曼光谱与部分最小方形 (PLS) 结合用于分析表面白色素的疗效.
为微区域文化遗产定量分析提供创新的技术支持.

主要方法:

准备30个模拟壁画样本,以优化色素颗粒大小和凝度.
从模拟的壁画样本中获取拉曼光谱.
使用光谱预处理和可变选择方法优化PLS校准模型,通过R2,RMSE,MRE,RSD和RPD进行评估.

主要成果:

两种PLS模型显示出优异的预测性能:Na2SO4的MSC-biPLS-PLS (Rp2 = 0.9635) 和CaCO3的MSC-siPLS-PLS (Rp2 = 0.9891).它们的预测性能都比较好.
模型实现了低误差 (RMSEp,MREp) 和高预测能力 (RPD>8.6).
随机样本中Na2SO4和CaCO3的恢复率分别为106.7%和111.3%.

结论:

拉曼光谱与PLS相结合,为壁画表面中关键盐的定量分析提供了一种高效准确的方法.
开发的模型提供了可靠的数据,以了解盐诱导的恶化机制.
这种技术显著提升了微区域分析能力,以保护文化遗产.

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