中药色谱指纹图谱的小波变换及分形表达方法

提出一种基于小波变换的色谱指纹图谱分形表达方法。该法运用小波变换将色谱谱线分解至不同分辨尺度，然后计算各尺度分量的分形维数，用色谱的小波基分形参量替代色谱指纹图谱的采样值。仿真实验结果表明，色谱小波基分形参量对谱峰保留时间漂移具有较好的抗干扰能力。以当归和川芎两种中药材的品种及道地性鉴别分类问题为实例，比较研究色谱采样值与小波基分形参量，k-近邻法的交叉验证计算结果表明，小波基分形参量的分类效果优于色谱采样值。     

小波神经网络用于色谱数据的滤噪与压缩

介绍了小波神经网络（Ｗａｖｅｌｅｔ Ｎｅｕｒａｌ Ｎｅｔｗｏｒｋ）的结构和算法，并将其应用于色谱数据的滤噪与压缩。在小波神经网络中，采用了Ｂｕｂｂｌｅ母小波和一维搜索变步长共轭梯度优化方法。结果表明，小波神经网络将原始数据压缩至１：２０时，仍能得到很好的重建谱图。重建的色谱图不仅能保留原始的谱图特征而且有光滑滤噪的效果。对于色谱数据的存储与平衡滤噪具有重要意义。     

小波变换用于植物激素重叠色谱峰中组分的定量分析

根据小波变换将信号按频率大小分解的性质，作者提出了一种用小波变换直接提取重叠色谱峰中单一组分信息的方法，并将其应用于植物激素混和体系重叠峰的解析与定量分析。结果表明：单组分的色谱信号经小波变换分解后仍能保持原有的线性关系，用小波变换从重叠信号中提取的单一组分信息也具有良好的线性关系。     

小波变换用于重叠色谱峰组分信息的提取

提出了一个小波变换用于提取重叠色谱中组分信息的理论公式，并将它应用于三组分和五组分重叠色谱实验数据的解析。结果表明：在定定分离度范围内，当相邻色谱峰峰宽相近时，根据此公式的计算结果，可以将各组分的信息从重叠色谱峰信号中提取出来。洒工作对小波变换用于重叠峰的解析具有重要意义。     

小波变换用于色谱重叠峰的解析

利用小波变换的时频局部化性质，通过对色谱重叠峰信号小波变换后的某些频率段进行放大，使重叠谱峰得到了分离，并将此方法用于苯和甲苯二组分色谱体系的定量分析，重叠峰中各组分均得到了良好的线性关系及令人满意的定量分析。本文还讨论了不同小波基、分解的次数及放大系数在解析结果的影响。     

小波变换及其有色谱重叠峰解析中的应用

本文介绍了小波分析的基本原理、计算方法及其在色谱重叠峰解析中的应用。显示了小波分析的特点，表明了小波分析用于重叠峰的解析与传统的方法相比具有其优越性。     

小波变换及其在色谱重叠峰解析中的应用

本文介绍了小波分析的基本原理、计算方法及其在色谱重叠峰解析中的应用。显示了小波分析的特点，表明了小波分析用于重叠峰的解析与传统的方法相比具有其优越性。     

基于小波和轮廓提取的色谱基线算法研究

把基于小波和轮廓提取的基线算法引入了色谱基线提取领域;基于轮廓提取的算法为:通过构造一个滑动窗,沿着色谱数据滑动求窗内的最小值。把这些最小值插值平滑就获得了色谱基线。基于小波和轮廓提取的算法为:先用小波初步提取基线,然后把色谱数据减去小波提取的基线后再用轮廓提取算法获得基线,把小波提取的基线和轮廓提取的基线相加即为原数据的基线。对这两个算法进行了比较实验研究,结果表明:基于小波和轮廓提取的算法比轮廓提取的算法效果好,能更准确地提取色谱基线。     

化学工业出版社新书介绍

《现代涂料仪器分析》 ，《分析化学中的小波分析技术》 ，《毛细管电泳技术及应用》， 《色谱在药物分析中的应用》 ，《色谱质谱联用技术》 ， 《农药分析》[编者按]     

小波变换用于高效液相色谱的基线校正

本文根据小波变换将信号分频的性质，用Ｄａｕｂｅｃｈｉｅｓ小波成功地解决了高效液相色谱中基线校正问题，用于定量分析乳酸－稀土络合物体系中的十六个稀土元素，所得结果与仪器给出了结果进行比较，准确度提高，重现性良好。     

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。     

An On-Line Wavelet Transform for De-Noising of High Performance Liquid Chromatograms

ABSTRACT

A novel algorithm of wavelet transform, called the on-line wavelet transform, which can be used in on-line processing of signals of an instrumental analysis, is proposed. And application of the on-line wavelet transform in denoising of high performance liquid chromatograms was investigated. Results showed that the noise in chromatograms was cleanly removed by the method, peak position after the on-line de-noising dose not change, and the linearity of calibration curves of concentration versus peak area remained and even was improved. Quantitative determination of three mixed samples were investigated with five standard samples; the recoveries were between 94.0-105.0%.     

基于迭代小波变换的光谱信号本底扣除方法研究

本底会对光谱分析结果产生很大的干扰作用,为获取特征峰的有效信息,必须首先去除本底。该文提出了一种基于小波变换的本底扣除算法,通过对光谱及后续光谱迭代进行小波变换,利用逼近系数估计本底,直到本底收敛。提出了判断多次估计的本底最大误差是否足够小的收敛准则。利用该算法去除本底后,即可进行特征峰信息的提取。分别利用仿真光谱和实验能量色散X射线荧光光谱对算法进行了验证,并与传统小波变换和多项式拟合法进行了对比。结果表明,该算法能够更准确扣除光谱本底,对其他光谱的本底扣除也具有借鉴意义。     

A continuous wavelet transform algorithm for peak detection

Contactless conductivity detector technology has unique advantages for microfluidic applications. However, the low S/N and varying baseline makes the signal analysis difficult. In this paper, a continuous wavelet transform-based peak detection algorithm was developed for CE signals from microfluidic chips. The Ridger peak detection algorithm is based on the MassSpecWavelet algorithm by Du et al. [Bioinformatics 2006, 22, 2059-2065], and performs a continuous wavelet transform on data, using a wavelet proportional to the first derivative of a Gaussian function. It forms sequences of local maxima and minima in the continuous wavelet transform, before pairing sequences of maxima to minima to define peaks. The peak detection algorithm was tested against the Cromwell, MassSpecWavelet, and Linear Matrix-assisted laser desorption/ionization-time-of-flight-mass spectrometer Peak Indication and Classification algorithms using experimental data. Its sensitivity to false discovery rate curve is superior to other techniques tested.     

Maximum spectrum of continuous wavelet transform and its application in resolving an overlapped signal

To estimate the number of peaks and to find the individual peak positions in an overlapped signal, a new method called maximum spectrum of continuous wavelet transform (MSCWT) was developed by extracting the maximum coefficients of continuous wavelet transform (CWT). The peak position in MSCWT was the same as that in its original signal. In this process, CWT was performed not on a single dilation but on an appreciation dilation range. To obtain such a range, a new criterion was introduced to choose a center dilation, which was used to form the dilation range. If Cdilation denoted the center dilation, the proper dilation range was [Cdilation -6 +/- 2, Cdilation +1 +/- 1]. The Mexican Hat function was an analytical wavelet. Utilizing the information of the peak number and the position detected by MSCWT, a fitting route was performed to recover the original signal. One simulated and four true overlapped signals, including high performance liquid chromatography (HPLC), ultraviolet-visible (UV) spectrum, and differential pulse voltammetric (DPV), were processed, and the results indicated that MSCWT could detect an overlapped peak number and position, and the curve fitting based on information of MSCWT had a higher accuracy. The proposed method was an efficient one in resolving different types of overlapped signals.     

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.     

基于多尺度小波变换的红外光谱谱峰识别算法

传统的谱峰检测方法一般分为3个步骤:谱线平滑、基线校正和谱峰识别.现有的基于小波变换的峰值检测方法能较好地将基线校正和谱峰识别两个步骤融为一步.在此基础之上,本研究将谱线平滑也很好地融入到小波变换的峰值检测算法中,使整个峰值检测算法成为一个整体.在峰值提取时,原始谱图直接处理,不再是处理加工过的谱图,减小了谱峰检测结果...     

基于小波-反向搜索及表面增强拉曼的食品中色素的光谱定性分析

使用金纳米粒子为增强因子的表面增强拉曼光谱技术,通过连续小波变换将拉曼光谱信号转化到小波空间(墨西哥帽小波作为小波基)。该步骤能够减轻信号中基线变化及随机噪音的影响并找到峰位置和最佳小波尺度系数。依据小波空间中的信息,对混合物光谱及标准谱光谱进行反向搜索得到反向搜索匹配系数(Reverse match quality,RMQ),作为判断混合物中目标成分是否存在的依据。该算法可对混合物中的目标物质进行准确定性,并已成功应用于多种食品中色素鉴定。食品中色素的检出率达到99%,且结果稳健,其效果明显优于传统的命中质量系数法(Hit quality index,HQI)。这证实了小波空间反向搜索方法是一种快速而准确的拉曼光谱定性算法。