GC-MS与交互移动窗口因子分析法(AMWFA)用于3种陈皮挥发油成分的比较分析

将GC-MS法与新近提出的用于二维数据比较和解析的交互移动窗口因子分析法(AMWFA)结合, 对3种不同原植物来源的陈皮挥发油成分的共性和差异性进行分析, 并对重叠峰进行解析, 总共分辨出138个峰, 定性出78个峰, 其中共有组分44个.     

窗口因子分析算法的改进及带噪声HPLCDAD数据的解析

窗口因子分析(WFA)是渐进过程二维数据解析的有力工具, 但信号中的噪声对解析结果有较强的干扰作用. 误差分析表明抽象因子分析(AFA)保留的噪声对解析结果有很大的影响. 提出了一种改进的WFA算法, 并对模拟和实验HPLC-DAD数据进行了解析. 结果表明, 改进的算法能够很大程度地克服噪声的影响, 改善由噪声而导致的峰形畸变, 从而提高了WFA处理带噪声数据的能力.     

子窗口分析法用于偶氮染料GC-MS重叠峰的解析

 将子窗口因子分析法 (SWFA)应用于GC MS联用检测数据分析 ,并以两种偶氮染料 3,3′ 二氯联苯胺和 4,4′ 次甲基 双 (2 氯苯胺 )重叠图谱为例进行了解析。结果表明 ,此方法可直接进行目标组分的质谱分辨 ,得到目标组分的质谱图 ,进而得到该组分的色谱图。其结果准确、可靠。与窗口因子分析相比 ,子窗口的选择更加容易 ,人工干预少 ,解析速度快。     

联用色谱数据的局部分辨

提出了一种对二维数据严重重叠峰中待测组份进行分辨的新方法：子窗口分析法（ｓｕｂｗｉｎｄｏｗ ａｎａｌｙｓｉｓ，ＳＡ）。该方法充分利用重叠区信息，成功地解析出严重重叠峰中待测组份的光谱，进而利用正交投影求得待测组份色谱曲线．这种对二维数据进行局部分辨的方法，降低了对色谱分离条件的要求，可直接应用于未知组份的定性定量分析。     

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。     

气相色谱/质谱结合化学计量学解析方法用于植物精油成分分析

使用气相色谱/质谱(GC-MS)法对缬草精油和含有缬草精油的混配精油的化学成分进行了分析.采用直观推导式演进特征投影法(HELP)对二维数据中的色谱重叠峰进行了解析,得到两种精油的各个物质的纯色谱和纯质谱,同时利用交互移动窗口因子分析法(AMWFA)直接比较两种精油中的共有组分,提取出共有组分的纯质谱,通过与HELP法解析出的质谱进行比较,发现AMWFA法比HELP法能更快速鉴别出混配精油中的缬草精油.     

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。     

小波变换-偏最小二乘算法及其在复方甲硝唑注射液分析中的应用

提出了结合小波变换的偏最小二乘法（ＷＰＬＳ）,即先对光谱信号进行小波变换,去除噪声,再用偏最小二乘法对多组分同时测定。将该法用于模拟体系及复方甲硝唑注射液体系,结果表明,该法优于偏最小二乘法。     

基于提升格式的小波变换用于处理化学信号

将提升haar小波变换应用于对不同类型含高噪声化学信号的处理,提出1种用于高噪声化学信号中滤除噪声的快速新方法——提升小波滤噪法,并使之与重叠峰分辨技术联用;以优选的小波分解层数对低信噪比的分析化学信号进行基于提升格式的小波变换处理,取得满意的结果;方法简单、快捷、准确、易行．运算速度是传统小波变换的一半,对高噪声化学信号的处理结果信噪比提高几百倍,峰位置相对误差小于1．5％;应用于氨基酸体系毛细管电泳检测信号的处理,有效降低了实验噪声的影响,分辨提取了难以察觉的信号．结果峰形变窄,峰高增加．大大提高了峰的分辨率．验证和显示了方法的可行性和优越性。     

利用减秩因子分析法同时优化确定化学反应级数和速率常数

通过对反应过程中在线测得的动力学谱-光谱二维数据矩阵进行主成分分析，可确定化学反应过程存在的组分数。提出用优化动力学参数-减秩因子分析法解析二维数据矩阵，对未知动力学模型的复杂反应可同时优化求解第一步反应的级数和速率常数。模拟二维数据验证了该方法的可行性。该方法用于高锰酸钾氧化溴化钠的反应过程中测得的二维数据的解析，结果表明：高锰酸钾的还原过程符合0级反应模型。     

Signals ratio method combined with wavelet transform: application to resolution of overlapped electrochemical signals

A signals ratio method combined with wavelet transform was proposed for the resolution of a weak voltammetric signal overlapped by other components. The signals ratio method usually suffers from interference from noise and baseline contained in the original signals because these factors cause distortion of the signals ratio. The multiresolution capability of the wavelet transform method was exploited here to simultaneously remove or reduce the noise and background. As a result, a deformation-free signals ratio with good signal-to-noise ratio (SNR) was obtained even for very noisy signals. The properties of the proposed method were compared to other resolution methods. It was demonstrated that the combined signals ratio wavelet transform method was particularly applicable to resolve a minor component in the presence of large amount of other components, suggesting that it can provide improved detection limits and quantified results for minor components. The method was employed for the voltammetric determination of residual chlorine in the presence of N,N-diethyl-p-phenylenediamine (DPD).     

Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry

Discrete wavelet transform (DWT) provides a well-established means for spectral denoising and baseline elimination to enhance resolution and improve the performance of calibration and classification models. However, the limitation of a fixed filter bank can prevent the optimal application of conventional DWT for the multiresolution analysis of spectra of arbitrarily varying noise and background. This paper presents a novel methodology based on an improved, second-generation adaptive wavelet transform (AWT) algorithm. This AWT methodology uses a spectrally adapted lifting scheme to generate an infinite basis of wavelet filters from a single conventional wavelet, and then finds the optimal one. Such pretreatment combined with a multivariate calibration approach such as partial least squares can greatly enhance the utility of Raman spectroscopy for quantitative analysis. The present work demonstrates this methodology using two dispersive Raman spectral data sets, incorporating lactic acid and melamine in pure water and in milk solutions. The results indicate that AWT can separate spectral background and noise from signals of interest more efficiently than conventional DWT, thus improving the effectiveness of Raman spectroscopy for quantitative analysis and classification.     

Wavelet transform as a new approach to the enhancement of signal-to-noise ratio in anodic stripping voltammetry

De-noising signals is a frequent aim achieved by signal processing in analytical chemistry. The purpose is to enable the detection of trace concentrations of analytes. The limit of detection is defined as the lowest amount of analyte that still causes signals greater than the background noise. Appropriate de-noising decreases only the noise and maintains the measurement signal, so that signal-to-noise ratios are enhanced. One adequate mean of signal processing for this purpose is wavelet transform, which still is not a common tool in analytical chemistry. In this paper, the ability of de-noising by wavelet transform is shown for measurements in anodic stripping voltammetry using a hanging mercury drop electrode. The calculation of limits of detection and signal-to-noise ratios on the basis of peak-to-peak noise is exercised to quantify the performance of de-noising. Furthermore, signal shape with regard of easing the application of base lines is discussed. Different wavelet functions are used, and the results are compared also to Fourier transform. Coiflet2 was found out to reduce noise by the factor of 330 and is proposed as the adequate wavelet function for voltammetric and similar signals.     

Wavelet transforms in separation science for denoising and peak overlap detection

Wavelet transform is a versatile time‐frequency analysis technique, which allows localization of useful signals in time or space and separates them from noise. The detector output from any analytical instrument is mathematically equivalent to a digital image. Signals obtained in chemical separations that vary in time (e.g., high‐performance liquid chromatography) or space (e.g., planar chromatography) are amenable to wavelet analysis. This article gives an overview of wavelet analysis, and graphically explains all the relevant concepts. Continuous wavelet transform and discrete wavelet transform concepts are pictorially explained along with their chromatographic applications. An example is shown for qualitative peak overlap detection in a noisy chromatogram using continuous wavelet transform. The concept of signal decomposition, denoising, and then signal reconstruction is graphically discussed for discrete wavelet transform. All the digital filters in chromatographic instruments used today potentially broaden and distort narrow peaks. Finally, a low signal‐to‐noise ratio chromatogram is denoised using the procedure. Significant gains (>tenfold) in signal‐to‐noise ratio are shown with wavelet analysis. Peaks that were not initially visible were recovered with good accuracy. Since discrete wavelet transform denoising analysis applies to any detector used in separation science, researchers should strongly consider using wavelets for their research.     

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。     

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.     

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.     

Comparative analysis of the volatile components in cut tobacco from different locations with gas chromatography-mass spectrometry (GC-MS) and combined chemometric methods

A combined approach of subwindow factor analysis and orthogonal projection resolution was used to analyze the volatile components of cut tobacco samples from different sources. After extracted with simultaneous distillation and extraction method, the volatile components in cut tobacco from five different locations were detected by GC-MS. Then, the qualitative and quantitative analysis of the volatile components of cut tobacco from Changde area was completed with the help of subwindow factor analysis resolving two-dimensional original data into pure mass spectra and chromatograms. One hundred and two volatile components among 138 separated peaks were identified and quantified, accounting for about 88.90% of the total content. Finally, orthogonal projection method was used to extract the common peaks from different locations. Among the identified components, there were 74 components coexisting in five studied samples although the relative content of each component showed difference to some extent. The results showed a fair consistency in their GC-MS fingerprints. It was the first time to apply orthogonal projection method to compare different cut tobacco samples, and it reduced the burden of qualitative analysis as well as the subjectivity. The obtained results proved the combined approach powerful for the analysis of complex cut tobacco samples. The developed method can be used to compare the sameness and differences of cut tobacco from different sources and for quality control of cigarette production and materials.     

小波变换用于示波信号中有用信息提取的研究

示波分析是近年来在我国发展起来的一个新的电化学分析研究领域［１～４］．它根据阴极射线示波器荧光屏上示波图及其变化进行分析测试,从原理上可以将其分为示波电位法和示波计时电位法;从测定方式上可以将其分为示波滴定和示波测定．关于示波电位法的一些理论问题（如...     

Theory of the wavelet analysis for electrochemical noise by use of the Laguerre functions

It is shown that the wavelet transform that uses the Laguerre function as a basis function is a useful tool to analyse the stationary electrochemical noise. Knowledge of the variance of the Laguerre wavelet of noise allows the Laplace transform of the correlation function to be found. The Laplace transform of the correlation function may be referred to the spectral density in the Laplace domain as well as to the operational spectral density of noise. It is shown that the operational spectral density of noise can be found not only by averaging over the ensemble of realizations of the noise process but also by averaging over the ensemble of Laguerre wavelets. The results obtained can be useful not only for analysis of electrochemical noise but also for analysis of any stationary random process, in particular for the time series analysis in econometric research.