Use of Raman microscopy and band-target entropy minimization analysis to identify dyes in a commercial stamp. Implications for authentication and counterfeit detection

Raman microscopy was used in mapping mode to collect more than 1000 spectra in a 100 microm x 100 microm area from a commercial stamp. Band-target entropy minimization (BTEM) was then employed to unmix the mixture spectra in order to extract the pure component spectra of the samples. Three pure component spectral patterns with good signal-to-noise ratios were recovered, and their spatial distributions were determined. The three pure component spectral patterns were then identified as copper phthalocyanine blue, calcite-like material, and yellow organic dye material by comparison to known spectral libraries. The present investigation, consisting of (1) advanced curve resolution (blind-source separation) followed by (2) spectral data base matching, readily suggests extensions to authenticity and counterfeit studies of other types of commercial objects. The presence or absence of specific observable components form the basis for assessment. The present spectral analysis (BTEM) is applicable to highly overlapping spectral information. Since a priori information such as the number of components present and spectral libraries are not needed in BTEM, and since minor signals arising from trace components can be reconstructed, this analysis offers a robust approach to a wide variety of material problems involving authenticity and counterfeit issues.     

Band-target entropy minimization. A robust algorithm for pure component spectral recovery. Application to complex randomized mixtures of six components

A newly developed self-modeling curve resolution method, band-target entropy minimization (BTEM), is described. This method starts with the data decomposition of a set of spectroscopic mixture data using singular value decomposition. It is followed by the transformation of the orthonormal basis vectors/loading vectors into individual pure component spectra one at a time. The transformation is based in part on some seminal ideas borrowed from information entropy theory with the desire to maximize the simplicity of the recovered pure component spectrum. Thus, the proper estimate is obtained via minimization of the proposed information entropy function or via minimization of derivative and area of the spectral estimate. Nonnegativity constraints are also imposed on the recovered pure component spectral estimate and its corresponding concentrations. As its name suggests, in this method, one targets a spectral feature readily observed in loading vectors to retain, and then combinations of the loading vectors are searched to achieve the global minimum value of an appropriate objective function. The major advantage of this method is its one spectrum at a time approach and its capability of recovering minor components having low spectroscopic signals. To illustrate the application of BTEM, spectral resolution was performed on FT-IR measurements of very highly overlapping mixture spectra containing six organic species with a two-component background interference (air). BTEM estimates were also compared with the estimates obtained using other self-modeling curve resolution techniques, i.e., SIMPLISMA, IPCA, OPA-ALS, and SIMPLISMA-ALS.     

Constrained nonlinear method for estimating component spectra from multicomponent mixtures

A method is described for estimating the spectra of pure components from the spectra of unknown mixtures with various relative concentrations. This method is based on principal component analysis and a constrained nonlinear optimization technique and is applicable to qualitative analysis of mixtures of more than three components. The method gives two curves as the estimate of a component spectrum: one consists of the set of the maxima and the other consists of the set of the minima for all sampling points subject to a priori information. Experimental results of the estimation of the infrared absorption spectra of xylene-isomer mixtures are shown; the noise problem with this method is also discussed.     

Spectral pattern recognition of in situ FT-IR spectroscopic reaction data using minimization of entropy and spectral similarity (MESS): application to the homogeneous rhodium catalyzed hydroformylation of isoprene

An improved algorithm using minimization of entropy and spectral similarity (MESS) was tested to recover pure component spectra from in situ experimental Fourier transform infrared (FT-IR) reaction spectral data, which were collected from a homogeneous rhodium catalyzed hydroformylation of isoprene. The experimental spectra are complicated and highly overlapping because of the presence of multiple intermediate products in this reaction system. The traditional entropy minimization method fails to resolve real reaction mixture spectra, but MESS can successfully reconstruct pure component spectra of unknown intermediate products for real reaction systems by the addition of minimization of spectral similarity. The quantitative measure of spectral similarity between two spectra was given by their inner products. The results indicate that MESS is a stable and useful algorithm for spectral pattern recognition of highly overlapped experimental reaction spectra. Comparison is also made between MESS, entropy minimization, simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), interactive principle component analysis (IPCA), and orthogonal projection approach-alternating least squares (OPA-ALS).     

Development of 2D band-target entropy minimization and application to the deconvolution of multicomponent 2D nuclear magnetic resonance spectra

Spectral reconstruction from multicomponent spectroscopic data is the frequent primary goal in chemical system identification and exploratory chemometric studies. Various methods and techniques have been reported in the literature. However, few algorithms/methods have been devised for spectral recovery without the use of any a priori information. In the present studies, a higher dimensional entropy minimization method based on the BTEM algorithm (Widjaja, E.; Li, C.; Garland, M. Organometallics 2002, 21, 1991-1997.) and related techniques were extended to large-scale arrays, namely, 2D NMR spectroscopy. The performance of this novel method had been successfully verified on various real experimental mixture spectra from a series of randomized 2D NMR mixtures (COSY NMR and HSQC NMR). With the new algorithm and raw multicomponent NMR alone, it was possible to reconstruct the pure spectroscopic patterns and calculate the relative concentration of each species without recourse to any libraries or any other a priori information. The potential advantages of this novel algorithm and its implications for general chemical system identification of unknown mixtures are discussed.     

Evaluation techniques for two-way data from in situ fourier transform mid-infrared reaction monitoring in aqueous solution

A model chemical reaction was monitored with in situ Fourier transform mid-infrared spectroscopy using an attenuated total reflectance probe. The evaluation of the IR spectra is complicated by the fact that the reaction runs in nonisothermal aqueous solution with large variations in pH. Despite this, it was possible to extract large amounts of useful information on the reaction after suitable pretreatment of the spectra. Alternating least-squares (ALS) multivariate curve resolution is shown to be a useful technique for obtaining pure component spectra and concentrations if suitable spectral regions are analyzed. Rank mapping methods are used as the basis for this sectioning into smaller regions. Techniques for finding and analyzing selective spectral regions are also shown to be applicable to this type of data. Partial least-squares (PLS) regression models based on spectral data were used to verify the results where possible. The correlation between the concentrations predicted from PLS and ALS is excellent.     

Identification of pure component spectra by independent component analysis in glucose prediction based on mid-infrared spectroscopy

We present a method for glucose prediction from mid-IR spectra by independent component analysis (ICA). This method is able to identify pure, or individual, absorption spectra of constituent components from the mixture spectra without a priori knowledge of the mixture. This method was tested with a two-component system consisting of an aqueous solution of both glucose and sucrose, which exhibit distinct but closely overlapped spectra. ICA combined with principal component analysis was able to identify a spectrum for each component, the correct number of components, and the concentrations of the components in the mixture. This method does not need a calibration process and is advantageous in noninvasive glucose monitoring since expensive and time-consuming clinical tests for data calibration are not required.     

Padé approximants and the modal connection: Towards increased robustness for fast parametric sweeps

To increase the robustness of a Padé‐based approximation of parametric solutions to finite element problems, an a priori estimate of the poles is proposed. The resulting original approach is shown to allow for a straightforward, efficient, subsequent Padé‐based expansion of the solution vector components, overcoming some of the current convergence and robustness limitations. In particular, this enables for the intervals of approximation to be chosen a priori in direct connection with a given choice of Padé approximants. The choice of these approximants, as shown in the present work, is theoretically supported by the Montessus de Ballore theorem, concerning the convergence of a series of approximants with fixed denominator degrees. Key features and originality of the proposed approach are (1) a component‐wise expansion which allows to specifically target subsets of the solution field and (2) the a priori, simultaneous choice of the Padé approximants and their associated interval of convergence for an effective and more robust approximation. An academic acoustic case study, a structural‐acoustic application, and a larger acoustic problem are presented to demonstrate the potential of the approach proposed.     

基于形态分量分析的滚动轴承故障诊断方法

在改进形态分量分析阈值去噪方法的基础上,提出了基于形态分量分析的滚动轴承故障诊断方法。形态分量分析根据信号中各组成成分的形态差异,构建不同的稀疏表示字典对各组成成分进行分离。当轴承出现局部损伤时,其振动信号往往由以包含轴承自身振动的谐振分量、包含轴承故障信息的冲击分量及随机噪声分量构成。谐振分量表现为信号中的平滑部分,而冲击分量则表现为信号中的细节部分,因此,可根据谐振分量与冲击分量的形态差异,实现二者的分离。本文方法利用形态分量分析对滚动轴承故障信号中的谐振分量、冲击分量和噪声分量进行分离,然后根据冲击分量中冲击之间的时间间隔诊断滚动轴承故障。算法仿真和应用实例表明,本文方法能有效地提取滚动轴承故障振动信号中的故障冲击成分。     

The multi-reconstruction entropy minimization method: unsupervised spectral reconstruction of pure components from mixture spectra, without the use of a priori information

The band-target entropy minimization method (BTEM) and its variant methods excel at reconstructing known/unknown pure spectra from mixtures without prior information. These mixtures may represent either non-reactive or even reactive systems. In this work, an unsupervised form of the entropy minimization curve resolution, namely, the multi-reconstruction entropy minimization method (MREM), is presented. MREM differs from BTEM by removing the need for band-targets and by introducing a multiple search routine to find multiple local entropy minima. This multiple search routine, which provides a rapid survey of spectral estimates, utilizes a localized form of Corona's simulated annealing method in the optimization. The objective functions and penalty functions of the BTEM type methods are essentially retained. Compared to BTEM type methods, MREM (1) searches for multiple local minima instead of a single global minimum and hence reconstructs many pure component spectra at once instead of one pure spectrum; and (2) utilizes a user-defined broad spectral range [v1, v2] for all searches instead of multiple user-defined narrow "targets" as in BTEM. The new MREM has been tested on four sets of real spectra using Fourier transform infrared spectroscopy (FT-IR), mass spectroscopy (MS), and ultraviolet-visible (UV-Vis) spectroscopy. The results show that MREM is computationally much faster than BTEM for finding the major components present. Also, because MREM does not rely on band targeting, it is very useful for spectra that have no localized features and are highly overlapping, such as UV-Vis.     

Combined use of conventional and second-derivative data in the SIMPLISMA self-modeling mixture analysis approach

Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) is a successful pure variable approach to resolve spectral mixture data. A pure variable (e.g., wavenumber, frequency number, etc.) is defined as a variable that has significant contributions from only one of the pure components in the mixture data set. For spectral data with highly overlapping pure components or significant baselines, the pure variable approach has limitations; however, in this case, second-derivative spectra can be used. In some spectroscopies, very wide peaks of components of interest are overlapping with narrow peaks of interest. In these cases, the use of conventional data in SIMPLISMA will not result in proper pure variables. The use of second-derivative data will not be successful, since the wide peaks are lost. This paper describes a new SIMPLISMA approach in which both the conventional spectra (for pure variables of wide peaks) and second-derivative spectra (for pure variables of narrow peaks, overlapping with the wide peaks) are used. This new approach is able to properly resolve spectra with wide and narrow peaks and minimizes baseline problems by resolving them as separate components. Examples will be given of NMR spectra of surfactants and Raman imaging data of dust particle samples taken from a lead and zinc factory's ore stocks that were stored outdoors.     

Attenuated total reflection infrared difference spectroscopy (ATR-IRDS) for quantitative reaction monitoring

Monitoring of chemical reactors is key to optimizing yield and efficiency of chemical transformation processes. Aside from tracking pressure and temperature, the measurement of the chemical composition is essential in this context. We present an infrared difference spectroscopy approach for determining the reactant (cyclooctene) and product (cyclooctane) concentrations during a catalytic hydrogenation reaction in the solvent cyclohexane, which is present in large excess. Subtracting the spectrum of the pure solvent from the reactor mixture spectra yields infrared (IR) spectra, which can ultimately be evaluated using a curve-fitting procedure based on spectral soft modeling. An important feature of our evaluation approach is that the calibration only requires recording the pure component spectra of the reactants, products, and solvent. Hence, no time-consuming preparation of mixtures for calibration is necessary. The IR concentration results are in good agreement with gas chromatography measurements.     

Second-derivative variance minimization method for automated spectral subtraction

A new second-derivative variance minimization (SDVM) procedure is used to automatically extract spectra of a dilute component (solute) from a mixture whose spectrum is dominated by a major component (solvent). This procedure involves the subtraction of Savitzky-Golay second-derivative preprocessed pure solvent and mixture spectra by minimizing the variance of the difference spectrum. The resulting undifferentiated output spectra contain primarily features associated with the solute and/or solute-induced perturbations of the solvent. The SDVM method is found to outperform several related methods, including a previously proposed derivative minimization method, as demonstrated using 1000 randomly generated solute/solvent synthetic spectral pairs and experimental Raman spectra of dilute solutions of benzene in n-hexane and water in acetone. The former experimental solution produced SDVM difference spectra containing benzene bands with virtually no n-hexane interference, while the latter revealed water-induced shifts in acetone spectral features. Several other types of SDVM applications, such as the spectroscopic analysis of layered composites, are discussed.     

Resiliency as a component importance measure in network reliability

This paper seeks to define the concept of resiliency as a component importance measure related to network reliability. Resiliency can be defined as a composite of: (1) the ability of a network to provide service despite external failures and (2) the time to restore service when in the presence of such failures. Although, Resiliency has been extensively studied in different research areas, this paper will study the specific aspects of quantifiable network resiliency when the network is experiencing potential catastrophic failures from external events and/or influences, and when it is not known a priori which specific components within the network will fail. A formal definition for Category I resiliency is proposed and a step-by-step approach based on Monte-Carlo simulation to calculate it is defined. To illustrate the approach, two-terminal networks with varying degrees of redundancy, have been considered. The results obtained for test networks show that this new quantifiable concept of resiliency provides insight into the performance and topology of the network. Future use for this work could include methods for safeguarding critical network components and optimizing the use of redundancy as a technique to improve network resiliency.     

Determination and resolution of non-UV-visible absorptive component during a kinetic process with modified soft modeling methods

Soft modeling (SM) methods can be used to resolve spectroscopic data from complicated reaction processes with unknown kinetics, with the exception of data containing a component for which there is no spectroscopic information available, such as an intermediate that does not absorb in the UV-visible region. In this work, modified SM methods were developed to resolve these undetectable components. Based on the mass balance principle, the mass balance error (MBE) method was first applied to determine whether the undetectable component existed. Next, the evolving error analysis (EEA) method was developed to search the local mass balance region (LMBR) where the concentration of the non-absorptive component was low enough to be neglected. In the LMBR, the concentration profiles of all absorptive components were scaled according to least squares regression. Subsequently, more reliable results were obtained using the evolving time region iteration (ETRI) method. Based on the mass balance principle, the concentration profile of the undetectable component was resolved for the entire time period. Both simulated and experimental data from an autocatalytic reaction were used to demonstrate the feasibility of the proposed method. In the autocatalytic oxidation of sodium oxalate by acidic potassium permanganate, the product Mn(II) was determined to be non-absorptive. Using the methods described above, the pure spectra of three other absorptive components and the scaled concentration profiles of four Mn species, including two intermediates, were all resolved. As a result, the mechanism of the reaction was more clearly described.     

Remote monitoring of a multi-component liquid-phase organic synthesis by infrared emission spectroscopy: the recovery of pure component emissivities by band-target entropy minimization

A liquid-phase cycloaddition reaction near ambient temperature involving dimethyl acetylenedicarboxylate (DMAD) and cyclopentadiene (CP) as reactants was measured using a conventional Fourier transform infrared (FT-IR) spectrometer with an emission accessory. Two semi-batch experiments were performed and a total of 55 spectra were collected using a DTGS detector. Band-target entropy minimization (BTEM), a pure component spectral reconstruction technique, was applied to analyze the data set to retrieve the pure component emission spectrum from the reaction system. The estimated emission spectra of the solvent chloroform, DMAD, CP, and product, namely dimethyl bicyclo[2.2.1]-2,5-heptadiene-2,3-dicarboxylate, were all reconstructed with rather good quality. The estimated emission spectra are similar to independent FT-IR spectra of the same cycloaddition reaction. Using a least squares fit, the relative concentration profiles of the species are obtained. Because this appears to be the first time that a liquid-phase reaction has been monitored by infrared emission spectroscopy, further improvements and opportunities for general multi-phase liquid reaction monitoring are discussed.     

Statistical analysis of excitation–emission matrices for laser-induced fluorescence spectroscopy

An algorithm for statistical processing of the set of multicomponent excitation–emission matrices for laser-induced fluorescence spectroscopy is proposed that is based on principal component analysis. It is shown for the first time that the fluorescence emission and excitation spectra of unknown fluorophores in optically thin samples can be calculated. Using the proposed algorithm, it is possible to pass from principal components with alternating signs to positive quantities corresponding to the spectra of real substances. The method is applied to a mixture of three fluorescent dyes, and it is demonstrated that the obtained spectra of principal components well reproduce the spectra of initial dyes.     

形态分量分析在转子早期碰摩故障诊断中的应用

提出了一种基于形态分量分析的转子早期碰摩故障诊断方法,该方法用形态分量分析从转子早期碰摩故障信号中提取出冲击成分。形态分量分析根据信号中各组成成分的形态差异,构建不同的稀疏表示字典对各组成成分进行分离。当转子系统中出现早期碰摩时,其振动信号往往由以转频及其谐波为主要成分的周期成分、包含转子早期碰摩故障信息的冲击成分及随机噪声构成。周期成分表现为信号中的平滑部分,而冲击成分则表现为信号中的细节部分,因此,可根据周期成分与冲击成分的形态差异,用形态分量分析实现二者的分离。对形态分量分析的阈值方法进行了改进,提出了基于半软阈值的形态分量分析,仿真结果表明,基于半软阈值的形态分量分析要优于基于硬阈值的形态分量分析。对某转子早期碰摩故障信号进行了分析,结果表明,基于半软阈值的形态分量分析能有效地提取转子早期碰摩故障信号中的冲击成分,进而诊断转子早期碰摩故障。     

弗德卡曼滤波阶比跟踪解耦新方法

摘要：弗德卡曼（Vold-Kalman）滤波阶比跟踪法是目前旋转机械阶比分析中能对阶比耦合干扰进行有效解耦操作的方法。但是传统的弗德卡曼升滤波解耦方法存在计算效率低,在无法充分获得耦合阶比瞬时频率信息时不能使用等不足。本文提出了一种基于独立分量分析技术的弗德卡曼滤波阶比跟踪解耦方法。其先将混合观察信号分解为阶比分量、耦合干扰等不同的独立信号分量,再在此基础上对分离出的阶比分量信号对应独立信号分量进行弗德卡曼滤波阶比跟踪分析,有效解决了传统解耦方法计算效率低,解耦需要干扰信号瞬时频率的不足。文中对弗德卡曼滤波阶比跟踪和独立分量分析的基本原理进行了简要介绍,在此基础上提出了本方法的实现方案。通过仿真试验和实际测试对本方法的有效性进行了评价。
     

主成分分析的入侵检测方法研究

直接将入侵检测算法应用在粗糙数据上,其入侵检测分析的效率非常低.为解决该问题,提出了一种基于主成分分析的入侵检测方法.该方法通过提取网络连接中的相关信息,对它进行解码,并将解码的网络连接记录与已知的网络连接记录数据进行比较,发现记录中的变化和连接记录分布的主成分,最后将机器学习方法和主成分分析方法结合实现入侵检测.实验结果表明该方法应用到各种不同KDD99入侵检测数据集中可以有效减少学习时间、降低各种数据集的表示空间,提高入侵检测效率.