Journal
APPLIED SPECTROSCOPY
Volume 74, Issue 4, Pages 427-438Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/0003702819888949
Keywords
Raman spectroscopy; convolutional neural network; CNN; preprocessing; simulated data; chemometrics; deep learning
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Funding
- Swedish Foundation for Strategic Research [ITM17-0056]
- Swedich Research Council [2016-04220]
- Swedish Foundation for Strategic Research (SSF) [ITM17-0056] Funding Source: Swedish Foundation for Strategic Research (SSF)
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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(5) 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.
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