A Fast, Automated, Polynomial-Based Cosmic Ray Spike–Removal Method for the High-Throughput Processing of Raman Spectra

2013 ◽  
Vol 67 (4) ◽  
pp. 457-462 ◽  
Author(s):  
H. Georg Schulze ◽  
Robin F.B. Turner
Keyword(s):  
Raman Spectra ◽  
High Throughput ◽  
Cosmic Ray ◽  
Removal Method

A Model-Free, Fully Automated Baseline-Removal Method for Raman Spectra

2011 ◽  
Vol 65 (1) ◽  
pp. 75-84 ◽  
Author(s):  
H. Georg Schulze ◽  
Rod B. Foist ◽  
Kadek Okuda ◽  
André Ivanov ◽  
Robin F. B. Turner
Keyword(s):  
Raman Spectra ◽  
Removal Method ◽  
Model Free ◽  
Baseline Removal

Single-Step Preprocessing of Raman Spectra Using Convolutional Neural Networks

2020 ◽  
Vol 74 (4) ◽  
pp. 427-438 ◽  
Author(s):  
Joel Wahl ◽  
Mikael Sjödahl ◽  
Kerstin Ramser
Keyword(s):  
Raman Spectra ◽  
Background Noise ◽  
Simulated Data ◽  
Cosmic Ray ◽  
Absolute Error ◽  
Single Step ◽  
Proof Of Concept ◽  
Baseline Subtraction ◽  
Synthetic Spectra ◽  
Asymmetric Least Squares

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 105 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.

Dielectric Stack Filters for Ex Situ and In Situ UV Optical-Fiber Probe Raman Spectroscopic Measurements

1997 ◽  
Vol 51 (11) ◽  
pp. 1722-1729 ◽  
Author(s):  
Calum H. Munro ◽  
Vasil Pajceni ◽  
Sanford A. Asher
Keyword(s):  
Raman Scattering ◽  
Optical Fiber ◽  
Raman Spectra ◽  
High Throughput ◽  
Single Stage ◽  
Fiber Probe ◽  
Optical Fiber Probe ◽  
Optical Fiber Probes ◽  
Uv Raman

Dielectric stack interference filters can be used in conjunction with a high-throughput single-stage spectrograph to facilitate the measurement of high signal-to-noise (S/N) ultraviolet (UV) Raman spectra with 228.9-nm and 244-nm excitation wavelengths. Placed between the sample and the spectrograph, these filters reflect Rayleigh scattering while transmitting Stokes-shifted Raman scattering. We have measured UV Raman bands from solid, highly scattering samples down to a 290-cm−1 shift from the Rayleigh line. The high throughput of the filtered single-stage spectrograph enables the measurement of UV Raman spectra from photo-labile samples, including DNA and the energetic materials pentaerythritol tetranitrate (PETN) and trinitrotoluene (TNT), with sufficiently low excitation powers and short accumulation times to minimize photo-alteration. High S/N UV preresonance and resonance Raman are obtained for PETN and TNT within 1 s, indicating the possible application of UV Raman spectroscopy as a rapid, highly selective screening methodology for the detection of trace levels of contraband explosives. Furthermore, the incorporation of these dielectric filters within a UV optical-fiber Raman probe head provides simultaneous Rayleigh rejection and removal of background silica Raman scattering. With the use of a 244-nm UV optical-fiber probe, we measured Raman spectra from 100 nM to 10 μM concentrations of polycyclic aromatic hydrocarbon (PAH) in water, even in the presence of an equimolar concentration of the visible fluorophore rhodamine 6G (R6G). Thus, we demonstrate the potential of UV Raman optical-fiber probes for minimally invasive in situ real-time monitoring at low analyte concentrations and within environments in which fluorescence backgrounds would prevent measurements with visible Raman optical-fiber probes.

A new paradigm for signal processing of Raman spectra using a smoothing free algorithm: Coupling continuous wavelet transform with signal removal method

2013 ◽  
Vol 44 (4) ◽  
pp. 608-621 ◽  
Author(s):  
Ahmad Esmaielzadeh Kandjani ◽  
Matthew J. Griffin ◽  
Rajesh Ramanathan ◽  
Samuel J. Ippolito ◽  
Suresh K. Bhargava ◽  
...  
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Raman Spectra ◽  
Continuous Wavelet Transform ◽  
Continuous Wavelet ◽  
New Paradigm ◽  
Removal Method

scholarly journals High-throughput screening Raman microspectroscopy for assessment of drug-induced changes in diatom cells

The Analyst ◽  
2019 ◽  
Vol 144 (15) ◽  
pp. 4488-4492 ◽  
Author(s):  
Jan Rüger ◽  
Abdullah Saif Mondol ◽  
Iwan W. Schie ◽  
Jürgen Popp ◽  
Christoph Krafft
Keyword(s):  
Raman Spectra ◽  
High Throughput ◽  
High Throughput Screening ◽  
Raman Microspectroscopy ◽  
Drug Induced ◽  
Localization Algorithms ◽  
P 16 ◽  
Induced Changes

16 888 Raman spectra were acquired with automated localization algorithms to investigate the effect of dithiothreitol on diatoms.

scholarly journals High-throughput computation and evaluation of raman spectra

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Qiaohao Liang ◽  
Shyam Dwaraknath ◽  
Kristin A. Persson
Keyword(s):  
Raman Spectra ◽  
High Throughput

154: Integration of TPSA and High-Throughput Mass Spectrometry Data Improves Prostate Cancer Prediction

2007 ◽  
Vol 177 (4S) ◽  
pp. 52-53
Author(s):  
Stefano Ongarello ◽  
Eberhard Steiner ◽  
Regina Achleitner ◽  
Isabel Feuerstein ◽  
Birgit Stenzel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mass Spectrometry ◽  
High Throughput ◽  
Mass Spectrometry Data ◽  
Cancer Prediction
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