Signal‐to‐noise ratio enhancement for Raman spectra based on optimized Raman spectrometer and convolutional denoising autoencoder

2021 ◽  
Author(s):  
Xian‐guang Fan ◽  
Yingjie Zeng ◽  
Yu‐Liang Zhi ◽  
Ting Nie ◽  
Ying‐jie Xu ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Denoising Autoencoder ◽  
Raman Spectrometer ◽  
Noise Ratio

scholarly journals Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4623
Author(s):  
Sinead Barton ◽  
Salaheddin Alakkari ◽  
Kevin O’Dwyer ◽  
Tomas Ward ◽  
Bryan Hennelly
Keyword(s):  
Raman Spectra ◽  
Loss Function ◽  
Signal To Noise Ratio ◽  
Biomedical Science ◽  
Clinical Tool ◽  
Signal To Noise ◽  
Low Snr ◽  
Low Intensity ◽  
Spectral Peaks ◽  
Noise Ratio

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

scholarly journals Recovery of Raman spectra with low signal-to-noise ratio using Wiener estimation

2014 ◽  
Vol 22 (10) ◽  
pp. 12102 ◽  
Author(s):  
Shuo Chen ◽  
Xiaoqian Lin ◽  
Clement Yuen ◽  
Saraswathi Padmanabhan ◽  
Roger W. Beuerman ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Wiener Estimation ◽  
Noise Ratio

scholarly journals Analysis of the alteration in the optical configuration of Raman spectrometer: Optimization of signal-to-noise ratio (SNR) in a specific wavelength range of clinical interest

2008 ◽  
Vol 22 (6) ◽  
pp. 467-474 ◽  
Author(s):  
João Carlos Lázaro ◽  
Carlos J. de Lima ◽  
Leonardo M. Moreira ◽  
Landulfo Silveira Jr. ◽  
Nelson J. F. da Silveira ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Spectral Lines ◽  
Optical Parameters ◽  
Signal To Noise ◽  
Minimum Width ◽  
Raman Spectrometer ◽  
System A ◽  
Optical Configuration ◽  
Noise Ratio

The present article is focused on the optimization of the optical parameters of a Raman spectrometer in order to obtain a minimum width of its spectral lines. In this way, using as reference the width of a fingerprint band of a calcified biological tissue, a spectral line without distortion or any loss of resolution was identified. This optimization is employed with the aim of improvement of the signal-to-noise ratio (SNR). A great improvement in the efficiency of the spectral collect was obtained, which can reduce significantly the time of diagnosis of target tissues, such as the calcified coronarian tissue. Therefore, the potential application of this new spectroscopic system increases the efficiency and precision, favoring the security of this technique to futurein vivoapplications. The excellent results obtained in this work become this spectroscopic system a powerful tool to the clinical diagnosis of several diseases.

The Fourier Self-Deconvolution of Raman Spectra

1985 ◽  
Vol 39 (6) ◽  
pp. 1004-1009 ◽  
Author(s):  
H. J. Bowley ◽  
S. M. H. Collin ◽  
D. L. Gerrard ◽  
D. I. James ◽  
W. F. Maddams ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Signal To Noise Ratio ◽  
Resolution Enhancement ◽  
Operating Conditions ◽  
Array Detector ◽  
Diode Array ◽  
Diode Array Detector ◽  
Signal To Noise ◽  
K Value ◽  
Noise Ratio

Resolution enhancement by the use of Fourier self-deconvolution has been achieved with Raman spectra obtained from an instrument with an intensified diode array detector. A minimum signal-to-noise ratio of about 500:1 is acceptable and this is readily attainable, by spectral accumulation, in the case of relatively strong peaks such as those of carbon tetrachloride at 549 cm−1 and tetrahydrofuran at 915 cm−1. Resolution enhancement factors, K, of about 2.7 are then achieved. Weaker peaks, typified by the v (C-Cl) modes of polyvinyl chloride) require more extensive spectral accumulation, but a K value of 2.2 has proved feasible. The finite resolution imposed by the diode array detector is not a significant limitation. In order to obtain consistently good results it is necessary to optimize the signal-to-noise ratio, by choosing instrumental operating conditions best suited to specific samples.

scholarly journals An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhongliang Yu ◽  
Lili Li ◽  
Wenwei Zhang ◽  
Hangyuan Lv ◽  
Yun Liu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Sleep Deprivation ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Mental Fatigue ◽  
Connectivity Analysis ◽  
Signal To Noise ◽  
Denoising Autoencoder ◽  
Feature Extraction Method ◽  
Noise Ratio

Mental fatigue is a common psychobiological state elected by prolonged cognitive activities. Although, the performance and the disadvantage of the mental fatigue have been well known, its connectivity among the multiareas of the brain has not been thoroughly studied yet. This is important for the clarification of the mental fatigue mechanism. However, the common method of connectivity analysis based on EEG cannot get rid of the interference from strong noise. In this paper, an adaptive feature extraction model based on stacked denoising autoencoder has been proposed. The signal to noise ratio of the extracted feature has been analyzed. Compared with principal component analysis, the proposed method can significantly improve the signal to noise ratio and suppress the noise interference. The proposed method has been applied on the analysis of mental fatigue connectivity. The causal connectivity among the frontal, motor, parietal, and visual areas under the awake, fatigue, and sleep deprivation conditions has been analyzed, and different patterns of connectivity between conditions have been revealed. The connectivity direction under awake condition and sleep deprivation condition is opposite. Moreover, there is a complex and bidirectional connectivity relationship, from the anterior areas to the posterior areas and from the posterior areas to the anterior areas, under fatigue condition. These results imply that there are different brain patterns on the three conditions. This study provides an effective method for EEG analysis. It may be favorable to disclose the underlying mechanism of mental fatigue by connectivity analysis.

Peak Detection Algorithm of Raman Spectra Based on Multi-Scale Local Signal-to-Noise Ratio

2014 ◽  
Vol 34 (6) ◽  
pp. 0630001
Author(s):  
姜承志 Jiang Chengzhi ◽  
孙强 Sun Qiang ◽  
刘英 Liu Ying ◽  
梁静秋 Liang Jingqiu ◽  
刘兵 Liu Bing
Keyword(s):  
Raman Spectra ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Signal To Noise ◽  
Multi Scale ◽  
Peak Detection Algorithm ◽  
Noise Ratio

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

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