Mostafapour Sara, Dörfer Thomas, Heinke Ralf, Rösch Petra, Popp Jürgen, Bocklitz Thomas
Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert‑Einstein‑Straße 9, 07745 Jena, Germany; Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany.
Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743 Jena, Germany.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Dec 5;302:123100. doi: 10.1016/j.saa.2023.123100. Epub 2023 Jul 1.
Raman reference libraries can be used for identification of components in unknown samples as Raman spectroscopy offers fingerprint information of the measured samples. Since Raman libraries often contain many different and/or highly similar spectra, it is important that the spectra are a reliable fingerprint for each compound. However, Raman spectra are highly sensitive to the experimental conditions, and the Raman spectra will change in different conditions even though the same sample is measured. Raman data pre-treatment minimizes the differences between Raman spectra arising from different experimental conditions. In this study, different combinations of pre-treatment methods are used to quantify the effect of each pre-treatment step in minimizing the differences between Raman spectra of the same sample in different experimental conditions, e.g., different excitation wavelengths. These different pre-treatment processes are evaluated for six solvents. The spectra differences between spectra recorded with three excitation wavelengths (532 nm, 633 nm, and 830 nm) are evaluated by angular difference index and the influence on a classification model is tested. The angular difference index of each spectrum after every data pre-treatment step shows a decreasing behavior. It could be demonstrated that wavenumber calibration has the largest effect on the differences between the Raman spectra. However, ω correction doesn't have a significate effect in this dataset. The classification results show that the prediction accuracy is improving by doing data pre-treatment. In the dataset obtained in 633 nm a lower amount of pre-treatment steps is needed but in the dataset 830 nm more pre-treatment steps are needed for a high accuracy. The result shows that the choice of an optimal pre-treatment method or combination of methods strongly influences the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis.
拉曼参考库可用于识别未知样品中的成分，因为拉曼光谱能够提供被测样品的指纹信息。由于拉曼库通常包含许多不同和/或高度相似的光谱，因此光谱是每种化合物的可靠指纹这一点很重要。然而，拉曼光谱对实验条件高度敏感，即使测量的是相同样品，在不同条件下拉曼光谱也会发生变化。拉曼数据预处理可最大限度地减少因不同实验条件而产生的拉曼光谱差异。在本研究中，使用不同的预处理方法组合来量化每个预处理步骤在最小化相同样品在不同实验条件下（例如不同激发波长）的拉曼光谱差异方面的效果。针对六种溶剂评估了这些不同的预处理过程。通过角度差异指数评估用三种激发波长（532 nm、633 nm和830 nm）记录的光谱之间的光谱差异，并测试其对分类模型的影响。每个数据预处理步骤后每个光谱的角度差异指数均呈现下降趋势。可以证明，波数校准对拉曼光谱之间的差异影响最大。然而，ω校正在该数据集中没有显著影响。分类结果表明，进行数据预处理可提高预测准确性。在633 nm获得的数据集中，所需的预处理步骤较少，但在830 nm的数据集中，为了获得高精度则需要更多的预处理步骤。结果表明，选择最佳的预处理方法或方法组合会强烈影响分析结果，但这远非易事，因为它取决于数据集的特征和数据分析的目标。
