Denoising applied to spectroscopies – Part II: Decreasing computation time

Laurent, Guillaume; Gilles, Pierre-Aymeric; Woelffel, William; Barret-Vivin, Virgile; Gouillart, Emmanuelle; Bonhomme, Christian

doi:10.1080/05704928.2018.1559851

Cited by 10 publications

(4 citation statements)

References 67 publications

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“…If complex numbers are used, V T , the transpose of matrix V, is replaced by V*, its conjugate transpose. SVD can indifferently be applied on real or complex matrices, the only difference being a double computation time for complex matrices (see part (II) of this work (86)). The central matrix Σ has the same shape as the original matrix X.…”

Section: C1 Svd and Low-rank Approximationmentioning

confidence: 99%

Denoising applied to spectroscopies – part I: concept and limits

Laurent

Woelffel

Barret-Vivin

et al. 2019

Applied Spectroscopy Reviews

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Some spectroscopies are intrinsically poorly sensitive, such as Nuclear Magnetic Resonance (NMR) and Raman spectroscopy. This drawback can be overcome by using Singular Value Decomposition (SVD) and low-rank approximation to denoise spectra and consequently increase sensitivity. However SVD limits have not been deeply investigated until now. We applied SVD to NMR and Raman spectra and showed that best results were obtained with a square data set in time domain. Automatic thresholding was applied using Malinowski's indicators. 6×7380 noisy spectra with 41 signal-to-noise ratios were compared to their nonnoisy counterparts, highlighting that SVD induces a systematic error for Gaussian peaks but faithfully reproduces shape of Lorentzian peaks, thus allowing quantification. Used carefully, SVD can decrease experimental time by a factor of 2.3 for spectroscopies. This study may help scientists to apply SVD to denoise spectra in a more efficient way, without falling into pitfalls.

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Section: C1 Svd and Low-rank Approximationmentioning

confidence: 99%

Denoising applied to spectroscopies – part I: concept and limits

Laurent

Woelffel

Barret-Vivin

et al. 2019

Applied Spectroscopy Reviews

Self Cite

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“…For example, researchers have developed a graphics processing unit (GPU) cooperating with optimized libraries of MATLAB, Java and Python to accelerate the singular value decomposition (SVD) denoising method. The running speed of the algorithm is strongly improved by combining the central processing unit (CPU) and GPU [25]. The result shows that the GPU methods can achieve a denoising method at a very fast speed (in 0.5 s), while the unoptimized CPU system runs at a far slower speed (in 90 s).…”

Section: Introductionmentioning

confidence: 99%

A Design of Real-Time Data Acquisition and Processing System for Nanosecond Ultraviolet-Visible Absorption Spectrum Detection

et al. 2022

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Ultraviolet-visible absorption spectroscopy is widely used to monitor water quality, and rapid optical signal detection is a key technology in the process of spectrum measurement. In this paper, an ultrafast spectrophotometer system that can achieve spectrum data acquisition in a single flash of the xenon lamp (within 200 ns) is introduced, and a real-time denoising method for the spectrum is implemented on a field programmable gate array (FPGA) to work cooperatively with the nanosecond spectrum acquisition system, in order to guarantee the quality of the spectrum signals without losing running speed. The hardware of the data acquisition and processing system are constructed on a Xilinx Spartan 6 FPGA chip and its peripheral circuit, including an analog to digital converter and a complementary metal-oxide-semiconductor transistor (CMOS) sensor’s diver circuit. An oversampling method that is suitable for the CMOS sensor’s output is proposed, which works on the CMOS sensor’s dark current noise and readout noise. Another moving-average filter method is designed adaptively, which works on the low-frequency component to filter out the residual spectrum noise of the spectrum signal. The implementation of the filter on the FPGA has been optimized by using a pipelined structure and dual high-speed random-access memory (RAM). As a result, the CMOS linear image sensor successfully captured the spectrum of xenon flash light at the readout clock frequency of 500 kHz and the processing manipulation to the full UV-Vis spectrum data was accomplished at a sub-microsecond speed performance. After the digital filter and oversampling technology were implemented, the coefficient of variation of the measurements reduced from 9.57% to 1.74%, while the signal noise ratio (SNR) of the absorption spectrum increased nine times, compared to the raw data of the CMOS sensor’s output. The tests towards different analyte samples were conducted, and the system shows good performance on distinguishing different concentrations of different analyte solutions on both ultra-violet and visible spectrum bands. The present work showcases the potential of the CMOS sensor’s technique for the fast detection of contaminated water containing nitrate and organic compounds.

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“…During Raman measurement, factors such as Raman scattering, fluorescence, cosmic rays, and the inherent noise of the whole system contribute to the output of the Raman spectrum . The existence of redundant information considerably interferes with the effective extraction of a weak Raman signal and then decreases the corresponding sensitivity . To solve these interferences, two different strategies have been used.…”

Section: Introductionmentioning

confidence: 99%

“…2 The existence of redundant information considerably interferes with the effective extraction of a weak Raman signal and then decreases the corresponding sensitivity. 17 To solve these interferences, two different strategies have been used. From the hardware part, the state-of-the-art improvement of both optical alignment design and optoelectronic devices of the Raman instrument is introduced.…”

Section: ■ Introductionmentioning

confidence: 99%

Developing a Peak Extraction and Retention (PEER) Algorithm for Improving the Temporal Resolution of Raman Spectroscopy

et al. 2021

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In spectroscopic analysis, push-to-the-limit sensitivity is one of the important topics, particularly when facing the qualitative and quantitative analyses of the trace target. Normally, the effective recognition and extraction of weak signals are the first key steps, for which there has been considerable effort in developing various denoising algorithms for decades. Nevertheless, the lower the signal-to-noise ratio (SNR), the greater the deviation of the peak height and shape during the denoising process. Therefore, we propose a denoising algorithm along with peak extraction and retention (PEER). First, both the first and second derivatives of the Raman spectrum are used to determine Raman peaks with a high SNR whose peak information is kept away from the denoising process. Second, an optimized window smoothing algorithm is applied to the left part of the Raman spectrum, which is combined with the untreated Raman peaks to obtain the denoised Raman spectrum. The PEER algorithm is demonstrated with much better signal extraction and retention and successfully improves the temporal resolution of Raman imaging of a living cell by at least 1 order of magnitude higher than those by traditional algorithms.

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Denoising applied to spectroscopies – Part II: Decreasing computation time

Cited by 10 publications

References 67 publications

Denoising applied to spectroscopies – part I: concept and limits

Denoising applied to spectroscopies – part I: concept and limits

A Design of Real-Time Data Acquisition and Processing System for Nanosecond Ultraviolet-Visible Absorption Spectrum Detection

Developing a Peak Extraction and Retention (PEER) Algorithm for Improving the Temporal Resolution of Raman Spectroscopy

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