Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany; Institute of Physical Chemistry and Abbe Centre of Photonics, Friedrich-Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany.
Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany; Institute of Physical Chemistry and Abbe Centre of Photonics, Friedrich-Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany; InfectoGnostics, Forschungscampus Jena, Philosophenweg 7, 07743 Jena, Germany.
Talanta. 2018 Aug 15;186:372-380. doi: 10.1016/j.talanta.2018.04.050. Epub 2018 Apr 22.
Fluorescence emission is one of the major obstacles to apply Raman spectroscopy in biological investigations. It is usually several orders more intense than Raman scattering and hampers further analysis. In cases where the fluorescence emission is too intense to be efficiently removed via routine mathematical baseline correction algorithms, an alternative approach is needed. One alternative approach is shifted-excitation Raman difference spectroscopy (SERDS), where two Raman spectra are recorded with two slightly different excitation wavelengths. Ideally, the fluorescence emission at the two excitations does not change while the Raman spectrum shifts according to the excitation wavelength. Hence the fluorescence is removed in the difference of the two recorded Raman spectra. For better interpretability a spectral reconstruction procedure is necessary to recover the fluorescence-free Raman spectrum. This is challenging due to the intensity variations between the two recorded Raman spectra caused by unavoidable experimental changes as well as the presence of noise. Existent approaches suffer from drawbacks like spectral resolution loss, fluorescence residual, and artefacts. In this contribution, we proposed a reconstruction method based on non-negative least squares (NNLS), where the intensity variations between the two measurements are utilized in the reconstruction model. The method achieved fluorescence-free reconstruction on three real-world SERDS datasets without significant information loss. Thereafter, we quantified the performance of the reconstruction based on artificial datasets from four aspects: reconstructed spectral resolution, precision of reconstruction, signal-to-noise-ratio (SNR), and fluorescence residual. The artificial datasets were constructed with varied Raman to fluorescence intensity ratio (RFIR), SNR, full-width at half-maximum (FWHM), excitation wavelength shift, and fluorescence variation between the two spectra. It was demonstrated that the NNLS approach provides a faithful reconstruction without significantly changing the spectral resolution. Meanwhile, the reconstruction is almost robust to fluorescence variations between the two spectra. Last but not the least the SNR was improved after reconstruction for extremely noisy SERDS datasets.
荧光发射是将拉曼光谱应用于生物研究的主要障碍之一。它通常比拉曼散射强几个数量级，并且阻碍了进一步的分析。在荧光发射过于强烈以至于无法通过常规数学基线校正算法有效地去除的情况下，需要采用替代方法。一种替代方法是位移激发拉曼差光谱法（SERDS），其中用两个略微不同的激发波长记录两个拉曼光谱。理想情况下，两个激发处的荧光发射不改变，而拉曼光谱根据激发波长移动。因此，在两个记录的拉曼光谱的差中去除了荧光。为了更好地解释，需要进行光谱重建过程以恢复无荧光的拉曼光谱。由于实验变化以及噪声的存在，两个记录的拉曼光谱之间的强度变化不可避免，因此这具有挑战性。现有的方法存在一些缺点，例如光谱分辨率损失、荧光残留和伪影。在本研究中，我们提出了一种基于非负最小二乘法（NNLS）的重建方法，其中利用了两个测量值之间的强度变化。该方法在没有明显信息损失的情况下，成功地对三个真实 SERDS 数据集进行了无荧光重建。此后，我们从四个方面基于人工数据集评估了重建的性能：重建的光谱分辨率、重建的精度、信噪比（SNR）和荧光残留。通过改变拉曼到荧光强度比（RFIR）、SNR、半峰全宽（FWHM）、激发波长偏移以及两个光谱之间的荧光变化来构建人工数据集。结果表明，NNLS 方法可以提供无明显光谱分辨率变化的忠实重建。同时，重建对两个光谱之间的荧光变化几乎具有鲁棒性。最后，对于极其嘈杂的 SERDS 数据集，重建后 SNR 得到了提高。
