毛细管电泳高频电导法同时测定阿莫西林和克拉维酸钾含量

毛细管电泳高频电导法同时测定阿莫西林和克拉维酸钾含量;毛细管电泳; 高频电导检测; 非接触式电导检测; 阿莫西林; 克拉维酸     

激光拉曼光谱解谱和处理的方法研究

研究一种拉曼光谱解谱和处理的方法。以化学计量学为基础,信号处理技术为工具,配合计算机算法的数据处理方法。具体为基线校正:对拉曼光谱原始信号进行基于自适应迭代重加权惩罚最小二乘法的基线校正;平滑:对进行完基线校正的拉曼光谱信号进行基于惩罚最小二乘法的平滑;峰检测:对进行完基线校正和平滑的信号进行基于连续小波变换的峰检测。这种基于惩罚最小二乘法的光谱平滑具有快速,可以连续控制平滑度并且可以进行交叉验证得到最客观的平滑值。改善了基于非对称最小二乘法的传统基线校正方法的两个缺陷。同时,基于连续小波变换的峰检测算法可以自动地并且同时考虑峰形和峰高对峰进行检测,最大限度地降低了峰检测假阳性的概率。     

极弱酸阴离子的不完全抑制电导检测离子色谱法

提出了一种新颖的用于测定极弱酸根阴离子的电导检测方法──不完全抑制电导法。从原理上分别与抑制电导法和非抑制电导法进行了比较。对亚砷酸硼酸的实验结果表明,不完全抑制电导法的检测限低于非抑制电导法。与抑制电导法相比,检测灵敏度有较大提高,对于250mg／L的硼酸,峰面积和峰高信号分别增大了1002倍和261倍。     

微芯片毛细管电泳电化学检测方法及应用

阐述了微芯片毛细管电泳电化学检测(包括安培法、电导法、电化学发光法和联用电化学法)的研究进展；对各种电化学检测的原理和应用进行了较详细的叙述；着重讨论了不同材料检测电极在安培检测中的应用；接触式电导和非接触式电导的应用情况；展望了微芯片毛细管电泳电化学检测的前景。引用文献87篇。     

基于养分指标相关性的土壤有机质预测模型研究

为探究土壤间各养分指标间的相互关系,选取了天津市宁河区不同区域的种植土壤,基于行业标准规定的检测方法,对主要养分指标进行了测定,并研究各指标间的相关性。结果表明,各区域土壤pH值变异系数最小,整体均呈碱性;有机质、全氮及速效钾变异系数属于中等;有效磷的变异系数最高。各研究区域土壤有机质含量与全氮含量呈显著正相关(P<0.01),与pH值呈显著负相关(P<0.01),而与其他指标间不具备普遍且显著的相关性。基于多元线性回归分析建立了土壤有机质与全氮及pH值之间的预测模型,通过模型计算出的预测值与实际测定值的拟合性较好;另取土壤样品进行验证表明,预测模型具有良好的可靠性和准确性。     

极谱曲线的背景扣除

提出一种借助微机准确、可靠扣除背景的运算方法,方法的拟合函数由背景函数B(E)=a/E+b+cE+dE~2和极谱峰函数(极谱峰电流方程与修正的高斯函数结合)加和构成,利用非线性最小二乘法参数估计,即可从实测的极谱曲线求得法拉第峰电流、峰电位和半峰宽度,而毋需空白溶液的数据,此法已应用于交流极谱和示差脉冲极谱。     

X射线光电子能谱中的分峰处理

文章详细介绍了X射线光电子能谱分峰操作的流程,对谱图中单峰和多峰的荷电位移、数据平滑、背景扣除、谱峰确认和多高斯拟合操作的数学流程进行了图解。我们认为,具有足够连续宽度和强度的峰值处才可以被判定为峰;谱图中可以进行含量定量的谱峰强度应在背景涨落的10倍以上;最后得到的分峰结果,还应进行统计检验。     

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.     

毛细管电泳非接触电导检测技术的新发展

毛细管电泳非接触电导检测(CE-CCD)是近年来发展迅速的一种检测技术。本文介绍了非接触式电导检测(CCD)的发展概况,着重阐述了CCD的原理、组成及应用情况。     

X射线衍射法钾混盐无标样定量相分析

采用EVD、TOPAS应用软件和TOPAS结构数据库以及无机晶体结构数据库,对一些混合盐样品(如钾混盐、尾矿等)进行了X射线粉末衍射无标样定量相分析全谱拟合模拟研究。在用Rietveld分析法对上述样品中无机盐成分的X射线衍射谱进行模拟过程中,选择基本参数函数为峰形函数,并通过March-Dolase择优取向校正拟合,提高了拟合精度。测得钾混盐样品中钾盐镁矾相含量为72.44%,岩盐相含量为27.56%,误差值Rwp(17.45),Rp(12.21)和拟合度S(4.91)均有所降低。     

Recursive Wavelet Peak Detection of Analytical Signals

A novel algorithm, entitled recursive wavelet peak detection (RWPD), is proposed to detect both normal and overlapped peaks in analytical signals. Recursive peak detection is based on continuous wavelet transforms (CWTs), which can be used to obtain initial peak positions even for overlapped peaks. Genetic algorithm (GA) and Gaussian fitting are used to refine peak parameters (peak positions, widths, and heights). Finally, area of peaks can be calculated by numeric integration. Simulated and ultrahigh performance liquid chromatographic ion trap time-of-flight mass spectrometry (UPLC-IT-TOF-MS) data sets have been analyzed by RWPD, MassSpecWavelet, and peakfit package by Tom O’Haver. Results show that RWPD can obtain more accurate positions and smaller relative fitting errors than MassSpecWavelet and peakfit, especially in overlapped peaks. RWPD is a convenient tool for peak detection and deconvolution of overlapped peaks, and it has been developed in R programming language and is available at https://github.com/zmzhang/RWPD.

     

Resolution of Overlapping Chromatographic Peaks Using a Genetic Algorithm

ABSTRACT

A genetic algorithm for resolution of overlapping chromatographic peaks (GAROCP) using real-number coding, non-uniform mutation and arithmetical crossover methods is described in this paper. It was applied to resolution of highly overlapped multicomponent high-performance liquid chromatographic peaks by fitting experimental chromatogram to the exponentially modified Gaussian (EMG) model. The genetic algorithm was used to find the minimum of fitting error to optimize the parameters in the EMG functions which determine the shape and area of each peak. The applicability of the method was investigated with both simulated signals calculated by EMG functions and experimental multicomponent overlapping chromatograms.     

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.     

A study of the precision and accuracy of peak quantification in comprehensive two-dimensional liquid chromatography in time

Simulated chromatographic data were used to determine the precision and accuracy in the estimation of peak volumes (i.e., peak sizes) in comprehensive two-dimensional liquid chromatography in time (LC × LC). Peak volumes were determined both by summing the areas in the second dimension chromatograms and by fitting the second dimension areas to a Gaussian peak. The Gaussian method is better at predicting the peak volume than the moments method provided there are at least three second dimension injections above the limit of detection (LOD). However, when only two of the second dimension signals are substantially above baseline, the accuracy and precision of the Gaussian fit method become quite poor because the results from the fitting algorithm become indeterminate. Based on simulations in which the modulation ratio (M_R = 4¹σ/t_s) and sampling phase (?) were varied, we conclude for well-resolved peaks that the optimum precision in peak volumes in 2D separations will be obtained when the M_R is between two and five, such that there are typically four to ten second dimension peaks recorded over the eight σ width of the first dimension peak. This sampling rate is similar to that suggested by the Murphy–Schure–Foley criterion. This provides an RSD of approximately 2% for the signal-to-noise ratio used in the present simulations. The precision of the peak volume of experimental data was also assessed, and RSD values were in the range of 4–5%. We conclude that the poorer precision found in the LC × LC experimental data as compared to LC may be due to experimental imprecision in sampling the effluent from the first dimension column.     

Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals part II: peak model and deconvolution algorithms

Several interlinked algorithms for peak deconvolution by non-linear regression are presented. These procedures, together with the peak detection methods outlined in Part I, have allowed the implementation of an automatic method able to process multi-overlapped signals, requiring little user interaction. A criterion based on the evaluation of the multivariate selectivity of the chromatographic signal is used to auto-select the most efficient deconvolution procedure for each chromatographic situation. In this way, non-optimal local solutions are avoided in cases of high overlap, and short computation times are obtained in situations of high resolution. A new algorithm, fitting both the original signal and the second derivatives is proved to avoid local optima in intermediate coelution situations. This allows achieving the global optimum without the need of background knowledge by the user. A previously reported peak model, a Gaussian with a polynomial standard deviation whose complexity can be modulated to enhance the fitting quality, was applied. However, the original formulation was modified to account baseline outside the peak region. Also, the optimal model complexity was auto-selected via error propagation theory. The method is able to process simultaneously several related chromatograms. The software was tested with both simulated and experimental chromatograms obtained with monolithic silica columns.     

Improved method for peak picking in matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

A method for peak picking for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) is described. The method is based on the assumption that two sets of ions are formed during the ionization stage, which have Gaussian distributions but different velocity profiles. This gives rise to a certain degree of peak skewness. Our algorithm deconvolutes the peak and utilizes the fast velocity, bulk ion distribution for peak picking. Evaluation of the performance of the new method was conducted using peptide peaks from a bovine serum albumin (BSA) digest, and compared with the commercial peak-picking algorithms Centroid and SNAP. When using the new two-Gaussian algorithm, for strong signals the mass accuracy was equal to or marginally better than the results obtained from the commercial algorithms. However, for weak, distorted peaks, considerable improvement in both mass accuracy and precision was obtained. This improvement should be particularly useful in proteomics, where a lack of signal strength is often encountered when dealing with weakly expressed proteins. Finally, since the new peak-picking method uses information from the entire signal, no adjustments of parameters related to peak height have to be made, which simplifies its practical use.     

Multi‐scale Gaussian/Haar wavelet strategies coupled with sub‐window factor analysis for an accurate alignment in nontargeted metabolic profiling to enhance herbal origin discrimination capability

Metabolic dataset can provide an overview of different herbal origin, which is conducted by some statistical procedures. Such results often deviate to a certain degree, due to peaks shifts in chromatographic signals. In order to solve this problem, an improved algorithm of combining sub‐window factor analysis with the mass spectrum information is proposed. The algorithm uses a peak detection approach derived either from multi‐scale Gaussian function or Haar wavelet to locate the peaks with different application scope; the candidate drift points at each peak are estimated by Fast Fourier transform cross correlation; Specifically, the best drift points at each candidate peaks are confirmed by sub‐window factor analysis and mass spectrum information in nontargeted metabolic profiling. Finally, the peak regions were aligned against a reference chromatogram, and the non‐peak regions were used linear interpolation. The chromatographic signals of 30 Bupleurum samples were aligned as an illustration of this algorithm, and they could be well distinguished using some statistical procedures. The result demonstrates that the presented method is stronger than other mass‐spectra based algorithms, when facing the alignment of some co‐eluted peaks.     

基于分形理论分辨重叠峰的新算法

将分形理论用于仪器分析信号的解析,提出一种面向分析谱图中重叠信号处理的分形分辨算法.通过对信号进行分形分析,采用分形维数可有效地反映信号的特征,准确地得到谱峰个数和位置的信息,避免人为判断的误差,实现重叠复合信号的分辨.实验表明,这种新的重叠谱峰分辨法能用于光谱、电化学、色谱等仪器分析数据的处理     

基于无基底扣除的数据趋势累积谱峰检测算法

谱峰的检测分析在色谱技术研究中具有十分重要的作用,但在色谱数据采集、传输的过程中,不同程度的噪声干扰给谱峰检测带来了极大的困难.目前传统的谱峰检测算法普遍通过基底扣除的方式对谱峰的形态进行预定义,将谱峰分为单峰、重叠峰等多个种类.针对不同种类的谱峰采用不同的检测方法,这就导致了传统的谱峰检测算法具有高复杂度、低自动化程...