Department of Fluid and Experimental Mechanics, Luleå University of Technology, Luleå, Sweden.
Appl Spectrosc. 2020 Apr;74(4):427-438. doi: 10.1177/0003702819888949. Epub 2020 Jan 21.
Preprocessing of Raman spectra is generally done in three separate steps: (1) cosmic ray removal, (2) signal smoothing, and (3) baseline subtraction. We show that a convolutional neural network (CNN) can be trained using simulated data to handle all steps in one operation. First, synthetic spectra are created by randomly adding peaks, baseline, mixing of peaks and baseline with background noise, and cosmic rays. Second, a CNN is trained on synthetic spectra and known peaks. The results from preprocessing were generally of higher quality than what was achieved using a reference based on standardized methods (second-difference, asymmetric least squares, cross-validation). From 10 simulated observations, 91.4% predictions had smaller absolute error (RMSE), 90.3% had improved quality (SSIM), and 94.5% had reduced signal-to-noise (SNR) power. The CNN preprocessing generated reliable results on measured Raman spectra from polyethylene, paraffin and ethanol with background contamination from polystyrene. The result shows a promising proof of concept for the automated preprocessing of Raman spectra.
（1）消除宇宙射线，（2）信号平滑，（3）基线扣除。我们表明，可以使用模拟数据训练卷积神经网络（CNN），以便在一次操作中处理所有步骤。首先，通过随机添加峰、基线、峰和基线与背景噪声的混合以及宇宙射线来创建合成光谱。其次，在合成光谱和已知峰上训练 CNN。预处理的结果通常比基于标准化方法（二阶差分、不对称最小二乘法、交叉验证）的参考结果质量更高。从 10 次模拟观测中，91.4%的预测具有更小的绝对误差（RMSE），90.3%具有更高的质量（SSIM），94.5%具有更低的信噪比（SNR）功率。该 CNN 预处理方法对来自聚乙烯、石蜡和乙醇的测量拉曼光谱以及来自聚苯乙烯的背景污染具有可靠的结果。该结果为拉曼光谱的自动预处理提供了有前景的概念验证。
