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...     

Rank annihilation factor analysis for spectrophotometric study of complex formation equilibria

The use rank annihilation factor analysis (RAFA) for spectrophotometric studies of complex formation equilibria are proposed. One-step complex formation and two successive and mononuclear complex formation systems studied successfully by proposed methods. When the complex stability constant acts as an optimizing object, and simply combined with the pure spectrum of ligand, the rank of original data matrix can be reduced by one by annihilating the information of the ligand from the original data matrix. The residual standard deviation (R.S.D.) of the residual matrix after bilinearization of the background matrix is regarded as the evaluation function. The performance of the method has been evaluated by using synthetic data. For two-step successive complex formation systems, the effects of noise level and equilibrium constants K₁ and K₂ on output of algorithm are investigated. The applicability of method for resolving the two-step successive complex formation systems with full spectral overlapping of two complex species also is shown. Spectrophotometric studies of murexide-calcium, dithiazone-nickel and methyl thymol blue (MTB)-copper are used as experimental model systems with different complexation stoichiometries and spectral overlapping of involved components.     

Introduction to spectral deconvolution

An introduction to the theory of Fourier self-deconvolution is given. Using this technique, the widths of bands in an infrared spectrum may be reduced to the point that resolution of completely overlapping spectral features can be affected. The effects of noise and side-lobes are discussed, and methods to minimize each are described. The procedure required to deconvolve a spectrum is illustrated by a practical example.     

Modified indirect hard modeling for non-invasive Raman measurements containing surface interference

Non-invasive Raman spectroscopy has been used in an increasing number of applications in recent years. However, in situations where surface signal is excessive, the acquired spectrum of probed sample suffers from surface interference in either conventional backscattering Raman or specially designed Raman methods. A computational method for Raman spectral recovery is required. Strong overlapping of Raman bands and intense fluorescence are the main obstacles that hinder the spectral recovery. In this paper, we present a modified version of an indirect hard modeling algorithm to extract the true Raman spectrum of the probed sample in a two-layer system. The proposed algorithm requires two spectra. By an iterative stepwise optimization, it models one spectrum as a combination of a scaling of the other spectrum, a polynomial baseline and the Raman peaks of the probed sample. It addresses the issue of Raman bands overlapping as well as intense fluorescence interference. The performance of the algorithm is evaluated on experimental Raman spectra. Comparative studies show that the proposed algorithm provides better results for Raman spectral recovery.     

差分拉曼光谱结合SVM对便签纸的鉴别分析

基于差分拉曼光谱技术与支持向量机(SVM)模型,提出了一种对便签纸类检材的快速可视化鉴别方法。实验获取了40组不同品牌便签纸样本的差分拉曼光谱数据,利用BP神经网络和差分技术完成谱图的除噪与基线校正后,借助F检验与主成分分析提取谱段信息,构建出SVM分类模型。实验结果表明,当设置Linear为SVM模型的核函数时,可以实现对样本测试集的完全准确划分,K折交叉验证的结果理想。相比于传统聚类分析手段,本方法可以在原始高维光谱数据中筛选出有效特征矩阵,且SVM模型兼具高效性和准确性,为公安实践中纸张类物证的区分鉴别提供一种新思路。     

An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data

A method is presented for extracting individual component spectra from gas chromatography/mass spectrometry (GC/MS) data files and then using these spectra to identify target compounds by matching spectra in a reference library. It extends a published “model peak” approach which uses selected ion chromatograms as models for component shape. On the basis of this shape, individual mass spectral peak abundance profiles are extracted to produce a “purified” spectrum. In the present work, ion-counting noise is explicitly treated and a number of characteristic features of GC/MS data are taken into account. This allows spectrum extraction to be reliably performed down to very low signal levels and for overlapping components. A spectrum match factor for compound identification is developed that incorporates a number of new corrections, some of which employ information derived from chromatographic behavior. Test results suggest that the ability of this system to identify compounds is comparable to that of conventional analysis.     

A simple method to extract spectral parameters using fractional derivative spectrometry

The nonlinear fitting method, based on the ordinary least squares approach, is one of several methods that have been applied to fit experimental data into well-known profiles and to estimate their spectral parameters. Besides linearization measurement errors, the main drawback of this approach is the high variance of the spectral parameters to be estimated. This is due to the overlapping of individual components, which leads to ambiguous fitting. In this paper, we propose a simple mathematical tool in terms of a fractional derivative (FD) to determine the overlapping band spectral parameters. This is possible because of several positive effects of FD connected with the behavior of its zero-crossing and maximal amplitude. For acquiring a stable and unbiased FD estimate, we utilize the statistical regularization method and the regularized iterative algorithm when a priori constraints on a sought derivative are available. Along with the well-known distributions such as Lorentzian, Gaussian and their linear combinations, the Tsallis distribution is used as a model to correctly assign overlapping bands. To demonstrate the power of the method, we estimate unresolved band spectral parameters of synthetic and experimental infra-red spectra.     

A software package for the generation of noise with widely divergent spectral properties : The Simulation of Realistic Stationary Detector Noise

The computer program discussed is capable of generating stationary noise with optional statistical parameters. Five standard noise types can be generated: white, 1/f, first order, Gaussian, and damped cosine noise. The input to the program can be a measured or arbitrarily chosen autocovariance function (or power spectrum). The autocovariance function (power spectrum) computed from the generated noise will be in perfect agreement with the input autocovariance function, if an infinite number of noise data is generated. Some theoretical work on noise generation on the basis of the autocovariance function is described. The autocovariance function of 1/f noise is theoretically derived from the power spectrum, based on a model described earlier. A few examples of the use of the program and an example of a possible application are given. A peak-finding procedure is tested with a simulated chromatogram contaminated with different types of noise. Applications are possible in data processing, information extraction, simulation and automation.     

基于多阶导数拉曼光谱组合技术的矿物油模式分类

为了实现对法庭科学领域重质矿物油物证的快速、准确、无损的鉴定,该文基于光谱分析技术提出了一种多阶导数光谱数据组合分析的方法。收集了80种不同型号、不同厂家的重质矿物油样本,利用傅里叶变换拉曼光谱分析法采集样本的原始光谱数据和导数光谱数据,并通过结合化学计量学构建分类模型。在构建的主成分分析(PCA)结合径向基函数神经网络(RBF)分类模型中,对单独的原始光谱、一阶导数谱和二阶导数谱数据的训练集准确率分别为80.0%、86.7%和86.2%,测试集准确率分别为73.3%、80.0%和72.7%;对组合后的原始光谱+一阶导数谱、原始光谱+二阶导数谱和一阶导数谱+二阶导数谱数据的分类中,训练集准确率分别为97.0%、96.7%和100%,测试集准确率分别为85.7%、90.0%和100%。结果表明,对组合后的导数光谱与原始光谱构建分类模型,准确率更高。其中,基于一阶导数谱+二阶导数谱数据构建的PCA结合RBF分类模型的结果最为理想,准确率达100%。而K最近邻算法模型由于受到样本不均匀的影响,整体分类准确率均较低。利用组合的导数光谱与原始光谱数据构建分类模型能够实现对重质矿物油样本的快速、准确、无损鉴别,可为光谱组合技术在法庭科学及其他分析测试领域的应用提供一定的借鉴和参考。     

A Background and noise elimination method for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of near infrared (NIR) spectral signals. The method is based on the use of multi-resolution, which is one of the main advantages provided by wavelet transform. The signals are firstly split into different frequency components, which keep the same data points of the original signals. In conjunction with a modified uninformative variable elimination (mUVE) criterion, the new method can be used to remove the low-frequency varying background and the high-frequency noise simultaneously. The method is successfully applied to simulated spectral data set and experimental NIR spectral data, resulting in more parsimonious multivariate models with higher precision. In addition, the proposed strategy can be applied to other spectral signals as well.     

Electrochemical noise spectroscopy: Method of secondary Chebyshev spectrum

A new method of electrochemical noise diagnostics is presented: the method of the secondary Chebyshev spectrum based on the splitting of an individual spectral line in the primary Chebyshev spectrum with formation of a system of spectral lines of the secondary Chebyshev spectrum. Algorithm for calculation of the secondary Chebyshev spectrum is developed. The suggested method based on analysis of noises measured in a specific electrochemical system is tested. It is shown that the new method allows determining the differences in the state of the electrochemical system more reliably, than the method of primary Chebyshev noise spectra.     

Constrained kernelized partial least squares

Nonlinear kernel methods have been widely used to deal with nonlinear problems in latent variable methods. However, in the presence of structured noise, these methods have reduced efficacy. We have previously introduced constrained latent variable methods that make use of any available additional knowledge about the structured noise. These methods improve performance by introducing additional constraints into the algorithm. In this paper, we build upon our previous work and introduce hard‐constrained and soft‐constrained nonlinear partial least squares methods using nonlinear kernels. The addition of nonlinear kernels reduces the effects of structured noise in nonlinear spaces and improves the regression performance between the input and response variables. Copyright © 2014 John Wiley & Sons, Ltd.     

A numerical scheme to diagnose interferences in gas chromatography-mass spectrometry quantitation of coeluting isotopically labeled and unlabeled counterparts with partially overlapping ion profiles

Quantitation of chromatographically coeluting compounds with partially overlapping mass profiles is a challenging task, especially if only a low-resolution mass spectrometer is available. To examine whether theoretical predictions can be utilized to determine the appropriate concentration ranges of the coeluting compounds that satisfy the non-interfering condition, we utilized an algorithm based upon a two-component model to compare the experimentally measured and predicted quantitation errors. Selected unlabeled and 13C-labeled polychlorinated biphenyl (PCB) congeners were investigated as model compounds. Standard solutions containing various concentration ratios of the unlabeled and 13C-labeled PCB congeners were analyzed, and the data were used to compare with theoretical predictions derived from the chlorine isotopic distributions (35Cl and 37Cl). Good agreements between experimental predictions and theoretical predictions were found on the magnitude of interferences for quantitation of 13C-labeled PCB congeners, as well as on the variability of the quantitation errors with the concentration ratio of 13C-labeled and unlabeled PCB counterparts. In addition, the magnitude of interferences considered in the present study was highly dependent upon the number of coexisting ions included for quantitation and their relative abundances in the mass spectrum. All these results suggest that the magnitude of interferences in quantifying a pair of coeluting compounds with partially overlapping mass spectral profiles can be effectively determined and minimized by carefully selecting the concentration ratio of the coeluting compounds and/or the number of quantitation ions. Finally, the selection of the experimental parameters to satisfy the non-interfering condition can be made purely on the basis of theoretical considerations.     

Determination of chlorogenic acid in plant samples by using near-infrared spectrum with wavelet transform preprocessing.

By theoretical analysis, it is found that wavelet transform (WT) with a wavelet function can be regarded as a smoothing and a differentiation process, and that the order of differentiation is determined by the vanishing moment, which is an important property of a wavelet function. Therefore, a method based on the continuous wavelet transform (CWT) for removing the background in the near-infrared (NIR) spectrum is proposed, and it is used in the determination of the chlorogenic acid in plant samples as a preprocessing tool for partial least square (PLS) modeling. It is shown that the benefit of the proposed method lies not only in its performance to improve the quality of PLS model and the prediction precision, but also in its simplicity and practicability. It may become a convenient and efficient tool for preprocessing NIR spectral data sets in multivariate calibration.     

Elimination of interference information by a new hybrid algorithm for quantitative calibration of near infrared spectra

A new hybrid algorithm is proposed to eliminate the interference information for multivariate calibration of near-infrared (NIR) spectra that includes noise, background and systemic spectral variation irrelevant to concentration. The method consists of two parts: approximate derivative based on continuous wavelet transform (CWT) and orthogonal signal correction (OSC). After the approximate derivative calculated by CWT, OSC was performed. It was successfully applied to real complex NIR spectral data to eliminate the interference information. Correction for the interference of NIR spectra resulted in a substantial improvement in the predicted precision, and a more concise calibration model was obtained. The proposed procedure also compared favourably with several pretreatment methods, and the new method appears to provide a high-performance pretreatment tool for multivariate calibration of NIR spectra. In addition, the strategy proposed here can be applied to various other spectral data for quantitative purposes as well.     

An approach for simultaneous estimation of reaction kinetics and curve resolution from process and spectral data

Parameter estimation of reaction kinetics from spectroscopic data remains an important and challenging problem. This study describes a unified framework to address this challenge. The presented framework is based on maximum likelihood principles, nonlinear optimization techniques, and the use of collocation methods to solve the differential equations involved. To solve the overall parameter estimation problem, we first develop an iterative optimization‐based procedure to estimate the variances of the noise in system variables (eg, concentrations) and spectral measurements. Once these variances are estimated, we then determine the concentration profiles and kinetic parameters simultaneously. From the properties of the nonlinear programming solver and solution sensitivity, we also obtain the covariance matrix and standard deviations for the estimated kinetic parameters. Our proposed approach is demonstrated on 7 case studies that include simulated data as well as actual experimental data. Moreover, our numerical results compare well with the multivariate curve resolution alternating least squares approach.     

多组分光谱相关色谱及其在中药色谱指纺图谱分析中的应用

基于正交投影理论和相关色谱方法提出多组分光色谱方法提出多组分光谱相关 色谱。为克服联用色谱仪器的不等性噪声影响，采用目标组分投影前后的光谱夹角 余弦判据，判断不同的中药色谱指纹图谱的相关组分色谱峰簇。该方法为中药指纹 图谱的数据处理、合理评价以及中药谱效学的基本理论研究提供一种实用的工具。     

High‐resolution peak analysis in TOF SIMS data

High mass resolution time‐of‐flight secondary ion mass spectrometry (TOF SIMS) can provide a wealth of chemical information about a sample, but the analysis of such data is complicated by detector dead‐time effects that lead to systematic shifts in peak shapes, positions, and intensities. We introduce a new maximum‐likelihood analysis that incorporates the detector behavior in the likelihood function, such that a parametric spectrum model can be fit directly to as‐measured data. In numerical testing, this approach is shown to be the most precise and lowest‐bias option when compared with both weighted and unweighted least‐squares fitting of data corrected for dead‐time effects. Unweighted least‐squares analysis is the next best, while weighted least‐squares suffers from significant bias when the number of pulses used is small. We also provide best‐case estimates of the achievable precision in fitting TOF SIMS peak positions and intensities and investigate the biases introduced by ignoring background intensity and by fitting to just the intense part of a peak. We apply the maximum‐likelihood method to fit two experimental data sets: a positive‐ion spectrum from a multilayer MoS₂ sample and a positive‐ion spectrum from a TiZrNi bulk metallic glass sample. The precision of extracted isotope masses and relative abundances obtained is close to the best‐case predictions from the numerical simulations despite the use of inexact peak shape functions and other approximations. Implications for instrument calibration, incorporation of prior information about the sample, and extension of this approach to the analysis of imaging data are also discussed.     

自适应拉曼光谱成像数据去噪及其在植物细胞壁光谱分析中的应用

拉曼光谱成像数据存在基线漂移与宇宙射线干扰峰两类噪声信号,无法直接用于光谱分析研究,必须去除。现有单光谱去噪方法处理结果不稳定、可重复性差。针对这一问题,本研究提出了一种自适应拉曼光谱成像数据新型去噪法,采用优化的自适应迭代惩罚最小二乘法( Adaptive iteratively reweighted penalized least-squares,airPLS)和基于主成分分析( PCA)的干扰峰消除算法修正光谱基线漂移和宇宙射线干扰峰,具有输入参数少、光谱失真小、处理速度快、去噪结果稳定等优点。利用本方法去除了芒草( Miscanthus sinensis)细胞壁拉曼光谱成像数据(9010条光谱)中的噪声信号,并对去噪后数据进行PCA和聚类分析(CA),成功区分非植物光谱与植物光谱,分类结果优于未去噪数据。预期本方法可应用于其它光谱成像数据去噪,为光谱的解译和定量分析提供可靠的研究基础。