Spline wavelet in the resolution of overlapping voltammetric peaks

Spline wavelet transform is used to resolve overlapping voltammetric peaks. A suitable resolving factor is chosen to multiply the filters of spline wavelet and make it a peak resoluter. Simulated overlapping voltammetric peaks are processed by the peak resoluter and satisfactory results are obtained. Base-line separation can be achieved, the relative errors of peak position are less than 3.0%, and the relative errors of peak area are less than 5.0%. The effect of different resolving factors and spline wavelet basis are discussed. To test the procedure, two systems, cadmium (Ⅱ)-indium (Ⅲ) and lead (Ⅱ)-thallium (Ⅰ), are used.     

Studies of spline wavelet least square in processing electrochemical signals

The application of spline wavelet least square (SWLS) in analytical chemistry signals is presented in this paper. As a new technique in signal processing, to extract useful signals from high noise, the influences of different parameters on the results of processing is discussed in details. If the suitable parameters are selected, useful signals can be filtered from the noise of S/N=0.5. The relative error of peak current is less than 3.0%, and that of peak potential is less than 10%. Comparison of this method with wavelet multifrequency channel decomposition (WMCD) and spline least square (SLS) has also been made and it indicates that SWLS can solve some problems in WMCD and SLS. The experimental results are also satisfactory.     

Spline wavelet analysis for voltammetric signals

Application of wavelet multifrequency channel decomposition (WMCD) in electroanalytical chemistry is presented in this paper. A new approach on this digital processing technique, to extract useful information from high noise signals in voltammetry, is described in detail. The method of constructing a model of B-spline WMCD and its application with 2nd, 3rd and 4th order B-spline in the linear scan voltammetry are thoroughly discussed and several results obtained. If the suitable optimal wavelet basis and frequency scale value are selected, the absolute values of the peak relative errors are less than 2% when the signal-to-noise ratio (S/N) is greater than 0.2, and the absolute values of the peak potential relative errors are less than 12% when S/N is greater than 0.3. The processed results of experimental data with high noise are also satisfactory. The whole computation is simple and needs shorter time than other signal processing methods.     

化学信号的近似高阶导数计算

提出了化学信号近似四阶导数计算的新方法——小波卷积法。该法通过信号与二阶样条小波函数的卷积运算对信号求导,能用于高噪音信号的直接求导,避免了普通导数运算将噪音放大的缺陷,即使对信噪比低至0．5的信号也能得到光滑的导数信号。详细讨论了尺度值、噪音、信号类型对求导的影响并建立了参数确定规则。将该法用于含噪音重叠分析化学信号的求导,能同时提高信号的分辨率和信噪比,结果满意。     

2nd-order spline wavelet convolution method in resolving chemical overlapped peaks

A 2nd-order spline wavelet convolution method in resolving overlapped peaks is developed. It determines the number of peaks, peak positions and width through wavelet's convolution, then uses spline function to construct the resoluter, which is used to resolve overlapped peaks. Theoretical proof is given, and the selections of wavelets and parameters are discussed. It is proven that baseline separation can be achieved after processed, the relative errors of peak position and area are less than 0.2% and 4.0% respectively. It can be directly applied to seriously overlapped signals, noisy signals and multi-component signals, and the results are satisfactory. It is a novel effective method for resolution.     

Study on spline wavelet self‐convolution in filtering and resolving overlapped voltammetric signals simultaneously

The normal distribution function is used as resolving factor and spline wavelet self‐convolution is used to resolve the overlapped peaks containing noise. The resolution of several kinds of overlapped peaks (SNR from 5 to 50) simulated by computer has been discussed in details. It is known that the overlapped peaks with noise can be separated (SNR > 50) directly and the noise is removed at the same time in time domain. Base‐line separation can be achieved. The relative errors of peak area and position are less than 5.0%. The satisfactory results are also obtained in resolving voltammetric overlapped signals by this method.     

连续样条小波变换用于分解重叠峰的研究

以B-样条小波为分析小波，提出了用于分析化学重叠信号解析的新方法——连续样条小波变换，结果表明：连续样条小波变换应用于分析化学信号的处理，能使峰变窄同时还能提高信号的信噪比，是一种新型有效的重叠信号解析方法，能从含噪声重叠信号中直接得到重叠峰的峰数目及其相应的峰位置。     

Quantification of components in overlapping peaks from capillary electrophoresis by using continues wavelet transform method

The application of continues wavelet transform (CWT) for resolving overlapping peaks from capillary electrophoresis (CE) is described. Overlapping peaks can be resolved easily by transforming experimental signals into their wavelet coefficients. The proposed method was applied for the determination of benzoic acid and salicylic acid in overlapping peaks from CE. The composition of the two acids in Zuguangsan, a traditional Chinese patent medicine, was determined. The quantification results are satisfactory. CWT has been shown to be a practicable approach for resolving overlapping peaks and for quantitative determining coeluted compounds in overlapping peaks from CE. The quantification results obtained from CWT were compared with those obtained from numerical differentiation method. CWT has been shown prior to numerical differentiation method for processing experimental signals which contain noise.     

基于小波神经网络的新型算法用于化学信号处理

基于紧支集正交小波神经网络的构造思想,用具有紧支集的Ｂ－样条函数的伸缩和平稳替代小波函数,提出了一种新型算法,并将其应用于化学信号的处理,实现了信号的压缩和滤噪,适应小波神经网络相比,学习速度得到了大幅度的提高。     

样条小波和Fourier变换联用技术在分析化学中的应用

探讨了样条小波和Fourier变换方法的算法及应用特征,将两种方法联合使用,设计出了一种新型的数字滤波器,使两种方法互相取长补短,使各自的优势能得到充分发挥.优选了处理低信噪比分析化学信号的滤波参数.不仅能使结果峰形高度保真,克服样条小波分析方法的不足,而且可对高噪声信号进行处理.     

Pattern recognition in capillary electrophoresis data using dynamic programming in the wavelet domain

A novel approach for CE data analysis based on pattern recognition techniques in the wavelet domain is presented. Low-resolution, denoised electropherograms are obtained by applying several preprocessing algorithms including denoising, baseline correction, and detection of the region of interest in the wavelet domain. The resultant signals are mapped into character sequences using first derivative information and multilevel peak height quantization. Next, a local alignment algorithm is applied on the coded sequences for peak pattern recognition. We also propose 2-D and 3-D representations of the found patterns for fast visual evaluation of the variability of chemical substances concentration in the analyzed samples. The proposed approach is tested on the analysis of intracerebral microdialysate data obtained by CE and LIF detection, achieving a correct detection rate of about 85% with a processing time of less than 0.3 s per 25,000-point electropherogram. Using a local alignment algorithm on low-resolution denoised electropherograms might have a great impact on high-throughput CE since the proposed methodology will substitute automatic fast pattern recognition analysis for slow, human based time-consuming visual pattern recognition methods.     

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.     

OWAVEC: a combination of wavelet analysis and an orthogonalization algorithm as a pre-processing step in multivariate calibration

In an spectroscopic context, when a calibration model based on partial least squares is developed to predict a response, it is often the case that a high percentage of variation in the data explained by the first latent variable is not accompanied by an equally high percentage of variation in the studied response. The addition of more components can slowly improve the calibration model, but with negative effects on the robustness and interpretability of the final model. To solve this problem, several pre-processing methods have been proposed to remove only a portion unrelated to the studied response from the spectral matrix.Moreover, the need for efficient compression methods is increasingly important due to the large size of the data currently collected. In this sense, discrete wavelet transform has proven that it can achieve good compression without losing relevant information when used on individual signals.This paper introduces a new pre-processing method, orthogonal wavelet correction (OWAVEC) that tries to lump together two important needs in multivariate calibration: signal correction and compression. The new method has been tested on a set of diesel fuels using viscosity as variable response, and its results have been compared not only with those obtained from original data but also with those provided by other correction methods. The first practical results are encouraging, as the method generates considerably better calibration models compared to the model developed from raw data and provides results as least so good as other orthogonal correction methods.     

Dedicated mother wavelet in the determination of antimony in the presence of copper

In this paper, a new signal-processing procedure is applied to the optimization of voltammetric determination of antimony in the presence of copper and the parallel determination of these two elements. The proposed numerical algorithm for the separation of the overlapping peaks utilizes the continuous wavelet transform and the inverse continuous wavelet transform. As the base function, the specially defined dedicated mother wavelet is used. In its construction the ideal, simulated voltammetric peak is intensively exploited. This approach, corresponding to the wavelet theory, gives satisfactory signals separation, even in the cases when they constitute one peak. The possibility of Sb(III) determination is presented in the case of different distances between copper and antimony peaks and also in 10-, 20- and 50-fold excesses of Cu. The parallel determination of Cu(II) and Sb(III) is possible even in the case of a 10-fold excess of copper. The quality of the obtained results fulfills the requirements of validation.     

阶跃伏安法中干扰信号的消除

以地阶跃伏安法中干扰信号的消除提出了一种新方法，即将条小波和ＲＬＴ滤波技术相结合既可以滤去随机噪声，又可以滤去充充电电流。方法效果较好。     

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· 3 H₂O and PrCl₃· 6 H₂O were obtained using the proposed CWT method; the results are satisfactory. Received: 21 December 1999 / Revised: 28 February 2000 / Accepted: 7 March 2000     

Multicomponent analysis of electrochemical signals in the wavelet domain

Successful applications of multivariate calibration in the field of electrochemistry have been recently reported, using various approaches such as multilinear regression (MLR), continuum regression, partial least squares regression (PLS) and artificial neural networks (ANN). Despite the good performance of these methods, it is nowadays accepted that they can benefit from data transformations aiming at removing baseline effects, reducing noise and compressing the data. In this context the wavelet transform seems a very promising tool. Here, we propose a methodology, based on the fast wavelet transform, for feature selection prior to calibration. As a benchmark, a data set consisting of lead and thallium mixtures measured by differential pulse anodic stripping voltammetry and giving seriously overlapped responses has been used. Three regression techniques are compared: MLR, PLS and ANN. Good predictive and effective models are obtained. Through inspection of the reconstructed signals, identification and interpretation of significant regions in the voltammograms are possible.     

The application of spline wavelet transform to the determination of electroosmotic mobility in capillary electrophoresis

The rule of current change was studied during capillary electrophoresis (CE) separation process while the conductivity of the sample solution was different from that of the buffer. Using a quadratic spline wavelet of compact support, the wavelet transforms (WTs) of capillary electrophoretic currents were performed. The time corresponding to the maximum of WT coefficients was chosen as the time of current inflection to calculate electroosmotic mobility. The proposed method was suitable for different CE modes, including capillary zone electrophoresis, nonaqueous CE and micellar electrokinetic chromatography. Compared with the neutral marker method, the relative errors of the developed method for the determination of electroosmotic mobility were all below 2.5%.     

Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods.     

Multicomponent quantitative resolution of binary mixtures by using continuous wavelet transform

Continuous 1-dimensional wavelet transform (WT) was applied to the quantitative analysis of a vitamin combination of thiamine hydrochloride (THI) and pyridoxine hydrochloride (PYR) with strongly overlapping signals. Absorbance data from the UV-Vis absorption spectrum of width 1150 were subjected to Gauss1 and Gauss2 WTs. Because of its flexibility, data processing, and its high signal amplitude, the continuous WT method is a powerful tool for analysis of multicomponent mixtures. By measuring the amplitude signals corresponding to the selected zero-crossing points of the transformed signal, we obtained the calibration curve. The validation of the calibration graphs was confirmed with different mixtures of THI and PYR at various concentration ratios. A brief explanation of the continuous wavelet method is given. MATLAB 6.5 software was used to perform the calculations. The results of our study were compared with those obtained by spectroscopic, chemometric, and liquid chromatographic methods, and good agreement was found.