无相移滤波技术用于分析化学信号处理

把无相移滤波技术引入了分析化学信号处理领域。其方法为：先将输入序列按顺序滤波，然后将所得结果逆转后反向通过滤波器，再将所得结果逆转后输出。通过对色谱信号的实验研究表明，与普通的数字滤波方法相比较，无相移滤波不但具有普通数字滤波的优点，而且不会产生滤波前后的相位偏移，具有良好的应用前景，尤其对于需要准确计算保留时间的场合，用此预处理方法非常适合。     

无相移滤波技术用于分析化学信号处理

把无相移滤波技术引入了分析化学信号处理领域.其方法为先将输入序列按顺序滤波,然后将所得结果逆转后反向通过滤波器,再将所得结果逆转后输出.通过对色谱信号的实验研究表明,与普通的数字滤波方法相比较,无相移滤波不但具有普通数字滤波的优点,而且不会产生滤波前后的相位偏移,具有良好的应用前景,尤其对于需要准确计算保留时间的场合,用此预处理方法非常适合.     

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。     

两种全光谱技术同时测定铜、镉、锌

建立了一种小波软阈值法（STWPLS）同时测定铜（Ⅱ）、镉（Ⅱ）、锌（Ⅱ）方法。此法结合多分辩分析和软阈值法改进除噪质量。通过最佳化，小波函数，低频收缩截止水平（L）和阈值法类型分别选为Daubechies8,3和HYBRID。并将此法与偏最小二乘法（PLS）做了比较。     

二进小波变换极大模法用于分析化学信号的滤噪

实验数据的滤噪在分析化学领域中具有重要的意义。小波变换技术具有很强的信号分离能力，容易把随机噪声从信号中分离出来，从而提高信号的信噪比。本文使用滤噪方法不同于传统离散小波变换方法，而是通过引入二进小波变换和李氏指数的概念，根据噪声与有用信号的极大模截然不同的特征，实现信号滤噪。实验数据的仿真结果研究也证明该方法的可行性。     

用于电分析化学重叠信号分辨的样条小波自卷积法

以正弦函数为分辨因子与样条小波滤波器相作用,使其构成样条小波自卷积法的峰分辨器,利用样条小波峰分辨器可以直接分开计算机模拟的不同类型的重叠双峰,分峰前后峰面积和峰位置的相对误差均小于5．0％;将其应用于电分析化学重叠信号的实验数据处理,得到较满意的结果。同时,为样条小波峰分辨器分辨因子提供了新的选择。     

一种基于二进小波变换的自适应滤波方法

根据信号和噪声经小波变换后在不同尺度上有不同的特征，将相邻尺度二进小波变换值的相关量进行归一化处理并与小波变换值比较来判断信号与噪声，以噪声在各尺度的方差作为终止迭代的标准，提出了一种基于二进小波变换小波域选择噪声的自适应滤波方法。研究了模拟信号的去噪过程、半峰宽和信噪比对去噪结果的影响，并对模拟含噪信号和含噪毛细管电泳信号去噪前后的结果进行了比较。实验结果表明：由于该方法具有良好的自适应性和显著的滤波效果，必将得到广泛的应用。     

化学计量学方法同时测定铁、铝和铍

采用正交信号校正(OSC)-小波包变换(WPT)-偏最小二乘法(PLS)(OSCW-PTPLS)相结合的化学计量学方法,用于不经化学分离解析光谱严重重叠的Fe(Ⅲ)、Al(Ⅲ)和Be(Ⅱ)混合物。该法结合OSC,WPT和PLS三种技术提高了获取特征信息的能力和回归质量。本文测定的三种金属离子可与铬天青S和溴化十六烷基吡啶(CPB)在pH=5.60的邻苯二甲酸氢钾-NaOH缓冲溶液中发生高灵敏度和低选择性的显色反应。设计了一个名为POSCWPTPLS的程序来执行相关计算。实验结果显示OSCWPTPLS方法优于PLS方法。     

A New Denoising Technique for Capillary Electrophoresis Signals

Capillary electrophoresis(CE) is a powerful analytical tool in chemistry,Thus,it is valuable to solve the denoising of CE signals.A new denoising method called MWDA which emplosy Mexican Hat wavelet is presented ,It is an efficient chemometrics technique and has been applied successfully in processing CE signals ,Useful information can be extractred even from signals of S/N=1 .After denoising,the peak positions are unchanged and the relative errors of peak height are less than 3%.     

小波变换与分析化学信号处理

介绍了小波变换的基本理论并对小波变换的常用算法和应用进行了评述。由于小波变换的时2频局部化性质, 使其成为信号处理的强有力工具。在分析化学领域中, 小波变换在流动注射分析、伏安分析、高效液相色谱、红外光谱、质谱、核磁共振谱、可见-紫外光谱、光声光谱、扩展X-射线吸收精细结构(EXAFS) 谱等分析化学信号的平滑滤噪、数据压缩、重叠信号解析等方面都有成功的应用。     

小波降噪技术在差分吸收光谱浓度检测中的应用

为了实现对工业气体SO₂的浓度进行监控,基于紫外差分吸收光谱法开发了SO₂在线检测系统。针对系统噪声和Mie散射使吸收光谱叠加带来的误差,本文提出采用小波变换降噪技术代替传统光谱处理方法中的多项式平滑滤波技术来提高检测精度。通过对应用了Symlets、Daubechies、Coiflet和Biorthogonal这4种不同小波函数的实验数据分析和对传统小波阈值选取方式的改进,最终确定了基于rigisure阈值的小波阈值去噪的信号处理方法,并提出一种新的信噪比量来衡量信号处理的效果。这种方法可以快速可靠地处理光谱信号,处理后所得的监测浓度准确度基本控制在1.5%以内。在实验室环境下和工业现场环境下的大量实验结果表明本方法能有效的减小噪声对SO₂浓度监测带来的影响。     

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.     

修正样条小波最小二乘法用于分辨高噪声重叠峰

采用连续样条小波变换确定重叠信号的组分峰的数目和峰位置,将样条小波最小二乘法加以改进,使其成为既能滤出噪声又能分峰的新方法.对信噪比为1的重叠峰进行处理,得到满意结果.该方法只需对信号进行一次处理,处理速度快,且数据点无须为2n(n∈Z).应用于毛细管电泳电导信号的处理,得到满意结果.     

FT-IR studies on the conformation and effective head-group area of AOT molecules in W/O microemulsions

Using Fourier transform infrared(FT-IR) spectroscopy technique, the carbonyl stretching vibration bands of AOT in sodium bis(2-ethylhexyl)sulfosuccinate (AOT)/isooctane/water reverse (W/O) microemulsions system have been investigated by least square curve fitting. The results indicate that an asymmetric adsorbed peak of carbonyl stretching vibration of AOT molecule is situated in (1739 ± 1) and (1725 ± 2) cm-1. The two peaks correspond to different carbonyls in gauche conformation and trans conformation of AOT molecules, respectively. With different water contents (W0), the variations of peak intensity ratio (/= l1739/l1725) reflect the change of the ratio for the two conformation populations and the variations of the effective head-group area of AOT molecule have relations to the ratio of two conformation populations.     

分析化学中光谱信号压缩方法

Telephonenumber:(852)-27665603Faxnumber:(852)-23649932E-mailaddress:bcftchau@hkpucc.polyu.edu.hk1IntroductionNowadays,n1ostofthechemicalinstrumentsarecomputerizedduetorapiddeveloI)mentofmodernmicroelectronicstechnology.Amicrocomputerisusuallyconnectedtoan…     

A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept

Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, expensive, and require significant sample preparation, which can alter the robustness of the analyses. In this context, we developed a fast, direct, and simple drop-coating deposition Raman imaging (DCDR) method combined with multivariate curve resolution alternating least square (MCR-ALS) to analyze glycosylation in monoclonal antibodies (mAbs). A database of hyperspectral Raman imaging data of glycoproteins was built, and the glycoproteins were characterized by LC-FLR-MS as a reference method to determine the composition in glycans and monosaccharides. The DCDR method was used and allowed the separation of excipient and protein by forming a “coffee ring”. MCR-ALS analysis was performed to visualize the distribution of the compounds in the drop and to extract the pure spectral components. Further, the strategy of SVD-truncation was used to select the number of components to resolve by MCR-ALS. Raman spectra were processed by support vector regression (SVR). SVR models showed good predictive performance in terms of RMSECV, R²_CV.     

Simultaneous deconvolution and re-construction of primary and secondary overlapping peak clusters in comprehensive two-dimensional gas chromatography

In this study, simultaneous deconvolution and reconstruction of peak profiles in the first ((1)D) and second dimension ((2)D) of comprehensive two-dimensional (2D) gas chromatography (GC×GC) is achieved on the basis of the property of this new type of instrumental data. First, selective information, where only one component contributes to the peak elution window of a given modulation event, is employed for stepwise stripping of each (2)D peak with the help of pure components corresponding to that compound from the neighbouring modulations. Simulation based on an exponentially modified Gaussian (EMG) model aids this process, where the EMG represents the envelope of all (2)D peaks for that compound. The peak parameters can be restricted by knowledge of the pure modulated (2)D GC peaks derived from the same primary compound, since it is modulated into several fractions during the trapping and re-focusing process of the cryogenic modulation system according to the modulation period. Next, relative areas of all pure (2)D components of that compound are considered for reconstruction of the primary peak. This strategy of exploitation of the additional information provided by the second dimension of separation allows effective deconvolution of GC×GC datasets. Non-linear least squares curve fitting (NLLSCF) allows the resolved 2D chromatograms to be recovered. Accurate acquisition of the pure profiles in both (1)D and (2)D aids quantification of compositions and prediction of 2D retention parameters, which are of interest for qualitative and quantitative analysis. The ratio between the sum of squares of deconvolution residual and original peak response (R(rr)) is employed as an effective index to evaluate the resolution results. In this work, simulated and experimental examples are used to develop and test the proposed approach. Satisfactory performance for these studies is validated by minimum and maximum R(rr) values of 1.34e-7% and 1.09e-2%; and 1.0e-3% and 3.0e-1% for deconvolution of (1)D and (2)D peaks, respectively. Results suggest that the present technique is suitable for GC×GC data processing.     

Combined Wavelet Transform with Curve‐fitting for Objective Optimization of the Parameters in Fourier Self‐deconvolution

Fourier self‐deconvolution was the most effective technique in resolving overlapping bands, in which deconvolution function results in deconvolution and apodization smoothes the magnified noise. Yet, the choice of the original half‐width of each component and breaking point for truncation is often very subjective. In this paper, the method of combined wavelet transform with curve fitting was described with the advantages of an enhancement of signal to noise ratio as well as the improved fitting condition, and was applied to objective optimization of the original half‐widths of components in unresolved bands for Fourier self‐deconvolution. Again, a noise was separated from a noisy signal by wavelet transform, therefore, the breaking point of apodization function can be determined directly in frequency domain. Accordingly, some artifacts in Fourier self‐deconvolution were minimized significantly.