分析化学计量学中的新方法——小波分析

小波分析在分析信号处理中具有诸多显著的优点。本文介绍了小波分析的一般描述，综述了化学计量学在的小波新方法，并展望了小波分析在分析化学计量学中的应用前景。     

化学计量学在电分析化学中的新进展

评述了化学计量学的各种方法,如主成分分析、偏最小二乘、小波分析、人工神经网络等在电分析化学中的进展,主要介绍了这些方法在电分析化学中的应用,并展望了化学计量学在电分析化学中的应用前景。     

《化学计量学方法》（第二版）简介

《化学计量学方法》（第二版）在第一版的基础上补充了最新出现的化学计量学研究方法和最新的研究成果。该书包括了化学计量学的主要内容，全书共分15章，分别为误差及与数理统计基础、回归分析、相关分析和数据平滑、最优化方法、主成分分析和因子分析、偏最小二乘方法、多元校正及分辨、小波分析、遗传算法和模拟退火算法、人工神经网络法及在化学中的应用、模拟识别方法、化合物结构表征和构效关系研究、组合化学、谱图库检索和结构解析专家系统及实验设计。此外，为方便读者查阅，该书还将常用数据信息列为附表。     

《分析化学中的小波分析技术》

小波变换的概念是由法国从事石油信号处理的工程师J．Morlet在1974年首先提出的并建立了反演公式。它解决了Fourier变换不能解决的许多困难问题，所以被誉为“数学显微镜”，具有分析发展史上里程碑式的意义。本书是分析科学现代方法丛书中的一册，是国内第一本介绍小波分析在分析化学中应用的图书。作者卢小泉教授自1994年始从事小波分析在分析化学中的应用研究，他在自己科研的基础上参阅了国内外大量的有关小波分析的文献后编著此书，书中内容具有很强的针对性和实用性。该书的出版能给分析化学工作者，特别是化学计量学工作者提供一些有价值的参考。     

化学计量学―光谱法在工农业生产中的应用

化学计量学是近几年发展迅速的化学量测方法。常用的化学计量学方法有小波变换、多元线性回归、偏最小二乘法、主成分分析、人工神经网络、遗传算法等。化学计量学方法与光谱法相结合广泛运用于工业生产、农业生产和环境监测等各个领域,体现出化学计量学在数据处理、信号解析等方面的重要作用。化学计量学―光谱法的建立为工、农业生产中多组分混合物的快速、准确测定及满足质量监控等要求提供了一条有效的途径。     

《分析化学中的小波分析技术》

小波变换的概念是由法国从事石油信号处理的工程师J．Morlet在1974年首先提出的并建立了反演公式。它解决了Fourier变换不能解决的许多困难问题，所以被誉为“数学显微镜”，具有分析发展史上里程碑式的意义。本书是分析科学现代方法丛书中的一册，是国内第一本介绍小波分析在分析化学中应用的图书。作者卢小泉教授自1994年始从事小波分析在分析化学中的应用研究，他在自己科研的基础上参阅了国内外大量的有关小波分析的文献后编著此书，书中内容具有很强的针对性和实用性。该书的出版能给分析化学工作者，特别是化学计量学工作者提供一些有价值的参考。     

《化学计量学方法》（第二版）简介

《化学计量学方法》(第二版)在第一版的基础上补充了最新出现的化学计量学研究方法和最新的研究成果。该书包括了化学计量学的主要内容,全书共分 15章,分别为误差及与数理统计基础、回归分析、相关分析和数据平滑、最优化方法、主成分分析和因子分析、偏最小二乘方法、多元校正及分辨、小波分析、遗传算法和模拟退火算法、人工神经网络法及在化学中的应用、模拟识别方法、化合物结构表征和构效关系研究、组合化学、谱图库检索和结构解析专家系统及实验设计。此外,为方便读者查阅,该书还将常用数据信息列为附表。《化学计量学方法》(第二…     

《化学计量学方法》（第二版）简介

《化学计量学方法》(第二版)在第一版的基础上补充了最新出现的化学计量学研究方法和最新的研究成果。该书包括了化学计量学的主要内容,全书共分15章,分别为误差及与数理统计基础、回归分析、相关分析和数据平滑、最优化方法、主成分分析和因子分析、偏最小二乘方法、多元校正及分辨、小波分析、遗传算法和模拟退火算法、人工神经网络法及在化学中的应用、模拟识别方法、化合物结构表征和构效关系研究、组合化学、谱图库检索和结构解析专家系统及实验设计。此外,为方便读者查阅,该书还将常用数据信息列为附表。《化学计量学方法》(第二版)作…     

小波分析和人工神经网络方法用于重叠色谱峰的解析

将神经网络与小波分析相结合,提出一种利用小波变换提取重叠色谱峰信息,再用人工神经网络解析的方法,为色谱峰重叠难于分开的物质的测定提供一种方法,提高了色谱分析的准确度,并将此方法应用于高效液相色谱中,建立了混合体系中3.4-二甲酚和2.5-二甲酚同时测定的新化学计量学方法.     

小波变换与神经网络结合用于示波计时电位测定

小波变换与神经网络的结合是化学计量学中一个十分活跃的研究领域。作者介绍了小波变换与神经网络的两种结合方式——辅助式结合和嵌套式结合,并将二者用于单组分示波计时电位信号定量计算的比较。结果表明：辅助式结合训练次数少,预测误差小;小波神经网络计算的自动化程度高,计算结果明显优于传统的人工神经网络。     

小波分析在化学中的应用进展

小波分析是信息处理的良好工具, 本文就其在化学中改善信号质量、图谱数据压缩、结构形态、模式识别、预测预报、过程控制、奇异诊断、动力学、量子化学和分形等方面的应用作了简要评述。     

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…     

Wavelet transform-based analysis for electrochemical noise

Wavelet transforms are presented as a useful tool to analyse electrochemical noise data. Various concepts developed in the framework of wavelet transforms have been adapted to study electrochemical noise measurements. The most relevant feature of this method of analysis is its capability of decomposing electrochemical noise records into different sets of wavelet coefficients, which contain information about corrosion events occurring at a determined time-scale. Thus, this mathematical approach could become an alternative tool which solves the limitations of other more established procedures for the analysis of electrochemical noise data, such as statistical or Fourier transform-based methods.     

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。     

Wavelet transform for Shah convolution velocity measurements of single particles and solutes in a microfluidic chip

Wavelet transform analysis is applied to determine the speed of fluorescent polystyrene microspheres and fluorescent solutes in a microchip. The data analysed consist of the periodical signal (Shah convolution) obtained when fluorescent particles or solute plugs move in a channel that is covered with a chromium grid pattern. This setup converts velocity into a (fluorescence emission) frequency, and previous analyses therefore used Fourier transform to extract the frequency information. In this paper it is shown that wavelet transform has some advantages over Fourier transform. With wavelet transform, time information can be obtained in addition to frequency information. Thus the speed of individual particles was determined together with their moments of appearance and disappearance in the system. With solutes small changes of velocity during the analysis were detected, and an improvement in peak frequency resolution was obtained.     

小波包变换潜变量回归分辨重叠的紫外光谱

采用小波包变换潜变量回归(WPLVR)方法,同时测定联苯、苯酚和邻苯二酚。该法结合小波包变换和潜变量回归改进除噪质量。通过最佳化,选择了小波函数及小波包分解水平(L)。编制了两个程序PWPLVR和PFTLVR进行WPLVR和付立叶变换潜变量回归(FTLVR)法计算。试验结果表明WPLVR法是成功的且优于FTLVR法。     

Wavelet transform-based Fourier deconvolution for resolving oscillographic signals

Fourier self-deconvolution is an effective means of resolving overlapped bands, but this method requires a mathematical model to yield deconvolution and it is quite sensitive to noises in unresolved bands. Wavelet transform is a technique for noise reduction and deterministic feature capturing because its time-frequency localization or scale is not the same in the entire time-frequency domain. In this work, wavelet transform-based Fourier deconvolution was proposed, in which a discrete approximation (such as A(2)) obtained from performing wavelet transform on the original data was substituted for the original data to be deconvolved and another discrete appropriate approximation (such as A(5)) was used as a lineshape function to yield deconvolution. Again, instead of the apodization function, the B-spline wavelet was used to smooth the deconvolved data to enhance the signal-to-noise ratio. As a consequence, this method does not suffer as badly as Fourier self-deconvolution from noises in the original data. Thus, resolution enhancement can be increased significantly, especially for signals with higher noise level. Furthermore, this method does not require a mathematical model to yield deconvolution; it is very convenient to deconvolve electrochemical signals.     

Enhanced feature analysis using wavelets for scanning probe microscopy images of surfaces

In this work we develop wavelet theory for the analysis of surface topography images obtained by scanning probe microscopy (SPM) such as atomic force microscopy (AFM). Wavelet transformation is localized in space and frequency, which can offer an advantage for analyzing information on surface morphology and topography. Wavelet transformation is an ideal tool to detect trends, discontinuities, and short periodicities on a surface. Additionally, wavelets can be used to remove artifacts and noise from scanning microscopy images. In terms of 3-D image analysis, discrete wavelet transform can capture patterns at all relevant frequency scales, thus providing a level of image analysis that is not possible otherwise. It is also possible to use the methodology for analyzing surface structures at the molecular level. The results demonstrate superior capabilities of wavelet approach to scanning probe microscopy image analysis compared to traditional analysis techniques.     

Rapid Determination of Metabolites in Bio‐fluid Samples by Raman Spectroscopy and Optimum Combinations of Chemometric Methods

The application of Raman spectroscopic techniques combined with multivariate chemometrics signal processing promise new means for the rapid multidimensional analysis of metabolites non‐destructively, with little or no sample preparation and little sensitivity to water. However, Rayleigh scattering, fluorescence and uncontrolled variance present substantial challenges for the accurate quantitative analysis of metabolites at physiological levels in biologically varying samples. Effective strategies include the application of chemometrics pretreatments for reducing Raman spectral interference. However, the arbitrary application of individual or combined pretreatment procedures can significantly alter the outcome of a measurement, thereby complicating spectral analysis. This paper evaluates and compares six signal pretreatment methods for correcting the baseline variances, together with three variable selection methods for eliminating uninformative variables, all within the context of multivariate calibration models based on partial least squares (PLS) regression. Raman spectra of 90 artificial bio‐fluid samples with eight urine metabolites at near‐physiological concentrations were used to test these models. The combination of multiplicative scatter correction (MSC), continuous wavelet transform (CWT), randomization test (RT) and PLS modeling presented the best performance for all the metabolites. The correlation coefficient (R) between predicted and prepared concentration reached as high as 0.96.