Suppressing the charged coupled device noise in univariate thin-layer videoscans: A comparison of several algorithms

The digital processing of chromatographic thin-layer plate images has increasing popularity among last years. When using a camera instead of flatbed scanner, the charged coupled device (CCD) noise is a well-known problem—especially when scanning dark plates with weakly fluorescing spots. Various techniques are proposed to denoise (smooth) univariate signals in chemometric processing, but the choice could be difficult. In the current paper the classical filters (Savitzky–Golay, adaptive degree polynomial filter, Fourier denoising, Butterworth and Chebyshev infinite impulse response filters) were compared with the wavelet shrinkage (31 mother wavelets, 3 thresholding techniques and 8 decomposition levels). The signal obtained from 256 averaged videoscans was treated as the reference signal (with noise naturally suppressed, which was found to be almost white one). The best choice for denoising was the Haar mother wavelet with soft denoising and any decomposition level larger than 1. Satisfying similarity to reference signal was also observed in the case of Butterworth filter, Savitzky–Golay smoothing, ADPF filter, Fourier denoising and soft-thresholded wavelet shrinkage with any mother wavelet and middle to high decomposition level. The Chebyshev filters, Whittaker smoother and wavelet shrinkage with hard thresholding were found to be less efficient. The results obtained can be used as general recommendations for univariate denoising of such signals.     

Signal denoising and baseline correction by discrete wavelet transform for microchip capillary electrophoresis

Signal denoising and baseline correction using discrete wavelet transform (DWT) are described for microchip capillary electrophoresis (MCE). DWT was performed on an electropherogram describing a separation of nine tetramethylrohodamine-5-isothiocyanate labeled amino acids, following MCE with laser-induced fluorescence detection, using Daubechies 5 wavelet at a decomposition level of 6. The denoising efficiency was compared with, and proved to be superior to, other commonly used denoising techniques such as Fourier transform, Savitzky-Golay smoothing and moving average, in terms of noise removal and peak preservation by directly visual inspection. Novel strategies for baseline correction were proposed, with a special interest in baseline drift that frequently occurred in chromatographic and electrophoretic separations.     

Comparison of methods to enhance flame ionization detector (FID) signals in gas chromatography

The performances of some numerical methods to improve the signal to noise ratio are compared and applied to enhance noisy signals obtained in gas chromatography with capillary columns and a flame Ionization detector. Several methods have been considered: cutoffs In the Fourier transform of the recorded signal; real time numerical filtering; theoretical model curve fitting; and the correlation of a chromatogram recorded from a pseudorandomly injected sample with the pseudorandom injection function. Numerical real time filtering is shown to be the most convenient method when the main periodic component of the noise has been determined by Fourier analysis.     

Adaptive approach for variable noise suppression on laser-induced breakdown spectroscopy responses using stationary wavelet transform

Spectral signals are often corrupted by noise during their acquisition and transmission. Signal processing refers to a variety of operations that can be carried out on measurements in order to enhance the quality of information. In this sense, signal denoising is used to reduce noise distortions while keeping alterations of the important signal features to a minimum. The minimization of noise is a highly critical task since, in many cases, there is no prior knowledge of the signal or of the noise. In the context of denoising, wavelet transformation has become a valuable tool. The present paper proposes a noise reduction technique for suppressing noise in laser-induced breakdown spectroscopy (LIBS) signals using wavelet transform. An extension of the Donoho's scheme, which uses a redundant form of wavelet transformation and an adaptive threshold estimation method, is suggested. Capabilities and results achieved on denoising processes of artificial signals and actual spectroscopic data, both corrupted by noise with changing intensities, are presented. In order to better consolidate the gains so far achieved by the proposed strategy, a comparison with alternative approaches, as well as with traditional techniques, is also made.     

微流控电泳芯片中化学发光信号的分段门限小波降噪

采用分段门限小波降噪(STWD)方法对化学信号中的异方差噪声进行降噪处理.用STWD法和统一门限小波降噪法同时处理两种模拟信号(其中之一包含异方差噪声).结果显示,优化参数的STWD法能够更有效地提高降噪效果.采用STWD法对微流控芯片化学发光检测信号中的异方差噪声进行处理,取得了满意的降噪效果.     

A partial least squares and wavelet-transform hybrid model to analyze carbon content in coal using laser-induced breakdown spectroscopy

A partial least squares (PLS) and wavelet transform hybrid model are proposed to analyze the carbon content of coal by using laser-induced breakdown spectroscopy (LIBS). The hybrid model is composed of two steps of wavelet analysis procedures, which include environmental denoising and background noise reduction, to pretreat the LIBS spectrum. The processed wavelet coefficients, which contain the discrete line information of the spectra, were taken as inputs for the PLS model for calibration and prediction of carbon element. A higher signal-to-noise ratio of carbon line was obtained after environmental denoising, and the best decomposition level was determined after background noise reduction. The hybrid model resulted in a significant improvement over the conventional PLS method under different ambient environments, which include air, argon, and helium. The average relative error of carbon decreased from 2.74 to 1.67% under an ambient helium environment, which indicated a significantly improved accuracy in the measurement of carbon in coal. The best results obtained under an ambient helium environment could be partly attributed to the smallest interference by noise after wavelet denoising. A similar improvement was observed in ambient air and argon environments, thereby proving the applicability of the hybrid model under different experimental conditions.     

小波包分析用于重叠分析化学信号的处理

对小波包分析的算法进行了改进,并将此算法成功地应用于多组分重叠色谱信号的解析.结果表明,本文提出的算法解决了MRSD算法的不足,更适合处理分析化学信号,用于重叠信号的解析时不需重构(逆变换),简化了数据处理步骤,加快了数据解析速度,具有较强的解析能力.对于重叠色谱信号的解析,小波包分析比小波分析具有更强的解析能力.     

一种新的小波滤波方法在化学谱图信号滤噪中的应用

仪器分析测定中，噪声的存在往往影响分析的准确度和仪器的检出限。小波变换多分辨分析的特性使得它成为一种很好的滤噪方法。基于小波分解后信号与噪声的小波系数随尺度变化规律不同的特性，提出了一种新的滤波滤方法-空域相关法，即通过不同尺度上相关系数模值与小波系数模模值的比较，达到滤波滤的目的。本文提出的方法具有无需人为选定无需人为选定滤噪阈值和小波函数、方法简单、失真度小等优点，可以大在提高信号的信噪比。模拟数据和ICP-AES实验数据证明了该方法的有效性。     

Enhancement of the Nuclear Spin Noise Signal Using Wavelet Transform

Spin noise spectroscopy has attracted considerable attention recently owing partly to intrinsic interest in the phenomenon and partly to its significant application potential. Here, we address the inherent problem of low sensitivity of nuclear spin noise and examine the utility of wavelet transform to mitigate this problem by distinguishing real peaks from the noise contaminated data. Suppression of the random circuit noise and the consequent enhancement of the correlated nuclear spin noise signal have been demonstrated with discrete wavelet transform. Spectra of both ¹H and ¹³C nuclear spins have been considered and significant signal enhancements in both the cases have been observed. A detailed analysis of several possible wavelet, thresholding and decomposition solutions have been made to obtain the optimum condition for signal enhancement. It is observed that the application of wavelet transform leaves the spin noise signal line shape essentially unchanged, which is an advantage for several applications involving spin noise spectra.     

自适应中值滤波用于色谱信号去噪的研究

在分析化学领域中，如何从被干扰的信号中有效去除噪声并恢复有用信号，具有重要的意义。此文从中值滤波基本概念出发，针对经典中值滤波器滑动窗口长度固定对信号去噪的影响，提出了自适应中值滤波器，通过对某元素色谱曲线进行去噪处理，并与移动均值法和小波变换法进行比较表明，此方法不仅能有效地滤除脉冲和高斯噪声，而且使色谱峰的边缘得到良好保护，从而提高了色谱数据分析的精确性。     

小波滤噪用于示波计时电位信号处理的研究

根据信号（Ｓ）和噪声（Ｎ）在小波变换下表现出的截然不同的性质,提出了一种新的滤噪方法,研究了噪音在不同细节的性质、滤噪阈值的确定及平滑与滤噪的特点。对不同Ｓ／Ｎ的示波计时电位信号进行了实验结果表明;小波滤噪在大大提高Ｓ／Ｎ的同时,信号强度基本不损失,优于传统的滤噪方法。     

去卷积伏安法中离散数据处理的方法研究(Ⅰ)──正交小波变换平滑

一系列的离散数据处理方法已成为化学计量学的重要组成部分[1],去卷和伏安法就是结合计算机技术的新一代电分析方法,其激励信号与输出信号均为计算机发生和采集的数字信号,对采集到的信号一般采用移动平均法[2]和Fourier变换处理法[3]进行平滑处理．但是,Fourier变换在电分析化学领域的难度较大,运算复杂,为此,Aubarel等[4]提出了不用FFT的Fourier变换平滑算法,但是该法要先对信号进行预处理,并且对Fourier的和式要反复进行折叠,计算量较大．80年代末发展起来的小波变换引起了人们广泛的关注[5],被称为数学“显微镜”,具有…     

The application of digital image recognition to the analysis of two-dimensional fingerprints

High-performance liquid chromatography (HPLC) fingerprint has been commonly used in the quality control and assessment of herbal medicines, and two-dimensional (2D) fingerprint obtained by means of HPLC-diode array detector (HPLC/DAD) can provide higher reliability. In this paper, an approach to the analysis of the 2D HPLC/DAD fingerprints, which was based on digital image recognition techniques, was developed for the first time. First, wavelet transform was employed to eliminate noise signal in the 2D fingerprint, and then the 2D fingerprint was converted into grayscale image. Second, several features of the image were calculated, and hierarchical clustering. This approach was applied to the qualitative analysis of the different samples of coptis chinensis, and the clustering result of samples was all highly consistent with the real situation. Based on the densities in grayscale image, three components in standard samples were quantitative analyzed, and the obtained correlation coefficients between concentration and grayscale density were more than 0.999. Our study indicated that the analysis of the 2D HPLC/DAD fingerprint was successful based on the idea and techniques of digital image recognition techniques, and this proposed approach provided a new pathway for the analysis of two-dimensional spectrums.     

小波变换用于近红外光谱性质分析

以汽油研究法辛烷值分析为例,研究了小波变换在近红外光谱分析中的应用。对近红外光谱的小波特性、小波变换参数以及变量提取方法进行了详细研究。研究结果表明：光谱噪音、有用信息和背景分别分布在小波高、中和低频区域;母小波函数对性质分析结果影响很大;小波变换可以同时扣除光谱背景、去除噪音和压缩变量,具有运算速度快、分析精度高以及无需去噪后处理等优点,在近红外光谱分析中具有很好的应用前景。     

Spatially adaptive stationary wavelet thresholding for the denoising of DNA capillary electrophoresis signal

The spatially adaptive thresholding based on the stationary wavelet transform was applied to the noise removal in the signal of DNA separated and determined by capillary electrophoresis. The threshold is derived in a Bayesian framework, and previously used on the wavelet coefficients is the generalized Gaussian distribution. The threshold is simple and closed form, which is adaptive to each subband because it depends on data-driven estimates of the parameters. Using this strategy, the noise in the signal of the DNA-separated analysis by capillary electrophoresis could be removed adequately. Experimental results show that the proposed denoising method is effective, and the spatially adaptive thresholding yields a lower root-mean-square error than the universal thresholding. The text was submitted by the authors in English.     

Noise filtering and deconvolution of XPS data by wavelets and Fourier transform

In experimental sciences, the recorded data are often modelled as the noisy convolution product of an instrumental response with the ‘true’ signal to find. Different models have been used for interpreting x‐ray photoelectron spectroscopy (XPS) spectra. This article suggests a method of estimate the ‘true’ XPS signal that relies upon the use of wavelets, which, because they exhibit simultaneous time and frequency localization, are well suited to signal analysis. First, a wavelet shrinkage algorithm is used to filter the noise. This is achieved by decomposing the noisy signal into an appropriate wavelet basis and then thresholding the wavelet coefficients that contain noise. This algorithm has a particular threshold related to frequency and time. Secondly, the broadening due to the instrumental response is eliminated through a deconvolution process similar to that developed in the previous paper in this series for the analysis of HREELS data. This step mainly rests on least‐squares and on the existing relation between the Fourier transform, the wavelet transform and the convolution product. Copyright © 2004 John Wiley & Sons, Ltd.     

Improving AFM images with harmonic interference by spectral analysis

Atomic force microscopy (AFM) is one of the most sensitive tools for nanoscale imaging. As such, it is very sensitive to external noise sources that can affect the quality of collected data. The intensity of the disturbance depends on the noise source and the mode of operation. In some cases, the internal noise from commercial AFM controllers can be significant and difficult to remove. Thus, a new method based on spectrum analysis of the scanned images is proposed to reduce harmonic disturbances. The proposal is a post-processing method and can be applied at any time after measurements. This article includes a few methods of harmonic cancellation (e.g., median filtering, wavelet denoising, Savitzky-Golay smoothing) and compares their effectiveness. The proposed method, based on Fourier transform of the scanned images, was more productive than the other methods mentioned before. The presented data were achieved for images of conductive layers taken in a contact AFM mode.     

利用小波变换检测电化学噪声信号波形

简述了应用小波变换检测点蚀电化学噪声信号的基本原理,对在3.5％ NaCl溶液中工业纯铝发生点蚀的电化学噪声信号检测分析表明：小波变换能够提取发生点蚀的电化学噪声信号和测量系统噪声在多惊讶分辨空间中的波形特征,根据表征该特征的小波系数模极大值在不同尺度下的传播特性,可实现对点蚀电化学噪声信号波形的检测。     

A novel method to calculate the approximate derivative photoacoustic spectrum using continuous wavelet transform

A novel method based on continuous wavelet transform (CWT) using Haar wavelet function for approximate derivative calculation of analytical signals is proposed and successfully used in processing the photoacoustic signal. An approximate nth derivative of an analytical signal can be obtained by applying n times of the wavelet transform to the signal. The results obtained from four other different methods--the conventional numerical differentiation, the Fourier transform method, the Savitzky-Golay method, and the discrete wavelet transform (DWT) method--were compared with the proposed CWT method; it was demonstrated that all the results are almost the same for signals without noise, but the proposed CWT method is superior to the former four methods for noisy signals. The approximate first and second derivative of the photoacoustic spectrum of Pr(Gly)3Cl3.3H2O and PrCl3.6H2O were obtained using the proposed CWT method; the results are satisfactory.     

分析化学信号在多尺度空间的滤噪

从多分辨率分析出发，结合离散正交小波变换(DWT)的理论，通过对各尺度下未抽取前的小波系数进行非线性滤波处理，达到在保护信号边缘的同时，有效实现白噪声及脉冲噪声的滤除。实验数据仿真结果研究也证明了该方法的可行性。