Abstract The weakened microstructure of archaeological wood (AW) objects from waterlogged environments necessitates consolidation to avoid anisotropic shrinkage upon drying. Polymer impregnation through submergence or spraying treatments is commonly applied, and for larger and thicker objects, the impregnation period can stretch over decades. Thus, for efficient treatment, continuous monitoring of the impregnation status is required. Today, such monitoring is often destructive and expensive, requiring segments for extraction and chromatographic quantification. This study proposes an in situ Raman spectroscopic method for quantification of polyethylene glycol (PEG) in waterlogged AW. A calibration model was built on standards of PEG, cellulose powder, and milled wood lignin using orthogonal partial least squares (OPLS). The OPLS model had a strong linear relationship, and the PEG content in wood of varying degrees of degradation could be determined. However, the accuracy of the model was low with a root mean square error of prediction of 11 wt%. The low accuracy was traced to the heterogeneity in the calibration and validation set samples with regard to the small probing volume of the confocal instrumental setup.

中文翻译：

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一种用于量化浸水考古木材中聚乙二醇 (PEG) 的原位拉曼光谱方法

摘要 来自浸水环境的考古木材 (AW) 物体的微弱微结构需要加固以避免干燥时的各向异性收缩。通常通过浸没或喷涂处理进行聚合物浸渍，对于较大和较厚的物体，浸渍期可能长达数十年。因此，为了有效处理，需要持续监测浸渍状态。如今，这种监测通常具有破坏性且成本高昂，需要用于提取和色谱定量的片段。本研究提出了一种原位拉曼光谱方法，用于量化浸水 AW 中的聚乙二醇 (PEG)。使用正交偏最小二乘法 (OPLS) 在 PEG、纤维素粉末和磨碎的木质素标准品上建立校准模型。OPLS模型具有很强的线性关系，可以确定不同降解程度木材中的PEG含量。然而，该模型的准确性较低，预测的均方根误差为 11 wt%。低准确度归因于校准和验证集样本的异质性，这与共聚焦仪器设置的小探测体积有关。