Multi‐component analysis: blind extraction of pure components mass spectra using sparse component analysis

The paper presents sparse component analysis (SCA)‐based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures to be greater than or equal to the unknown number of pure components. Specifically, we have demonstrated experimentally the capability of the SCA to blindly extract five pure components mass spectra from two mixtures only. Two approaches to SCA are tested: the first one based on ?₁ norm minimization implemented through linear programming and the second one implemented through multilayer hierarchical alternating least square nonnegative matrix factorization with sparseness constraints imposed on pure components spectra. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm together with pure components concentration matrix. Proposed methodology can be implemented as a part of software packages used for the analysis of mass spectra and identification of chemical compounds. Copyright © 2009 John Wiley & Sons, Ltd.     

Estimation of source spectra profiles and simultaneous determination of polycomponent in mixtures from ultraviolet spectra data using kernel independent component analysis and support vector regression

A method that use kernel independent component analysis (KICA) and support vector regression (SVR) was proposed for estimation of source ultraviolet (UV) spectra profiles and simultaneous determination of polycomponents in mixtures. In KICA-SVR procedure, the UV source spectra profiles were estimated using KICA, then the mixing matrix of the components were calculated using the estimated sources, and the calibration model was build using SVR based on the calculated mixing matrix. A simulated UV dataset of three-component mixtures was used to test the ability of KICA for estimating source spectra profiles from spectra data of mixtures. It was found that KICA has the potential power to estimate pure UV spectra profiles, and correlation coefficient of estimated sources correspond to the real adopted ones are better compared with that by FastICA and Infomax ICA. An UV dataset of polycomponent vitamin B was processed using the proposed KICA-SVR method. The results show that the estimated source spectra profiles are correlative with the real UV spectra of the components and chemically interpretable, and accurate results were obtained.     

On the extraction of spectra of components from spectra of mixtures. A development in factor theory—I

The theory of factor analysis is extended to permit the computer based extraction of the spectra of pure components from spectra of mixtures. A simultaneous linear, non-orthogonal transformation of the eigenspectra and eigenvectors is used to generate all positive absorbances and concentrations within noise level. Applications to actual spectra of mixtures of: (A) ethyl ethanoate, butan-2-one and oct-1-ene; (B) ethyl ethanoate, butan-2-one and cyclohexane; and (C) butan-2-one, cyclohexane and 2-methylpentane are presented. The effects of changes in signs of the constrained diagonal elements of the transformation matrix is investigated. To explore the ability of the software to cope with component spectra in which there are no wavenumbers at which one component dominates an artificial set of mixtures is examined. Recognizable spectra are extracted. It appears that, even when there are no wavenumbers at which one component dominates, the condition of finding “the sum of squares of negative absorbances/concentrations” near zero is adequate to yield useful spectra for library searches.     

Multicomponent analysis in luminescence spectroscopy

Multicomponent analysis of absorption spectra (infrared or electronic spectra) by factor analysis is a suitable method to use. The algorithm of factor analysis is now available for luminescence spectroscopy. The first possibility, as in absorption spectroscopy, is to determine pure components emission spectra for a unique set of spectra of mixtures in overdetermined conditions. On the other hand, it is also possible to extract pure components emission and excitation spectra of the only data corresponding to the 3D fluorescent spectrum of a single mixture. The results are evaluated in respect to noise level and concentrations ratio on both simulated and real spectra.     

A correlation method in library search

The identification of unknown pure compounds or mixtures by means of mass spectral library search can be improved by partly resolving the spectra of the individual components within the spectrum of the measured unknown. This is accomplished by investigating sequential spectra in series of spectra; masses with highly correlating sequential intensities are clustered into individual groups. On the assumption that correlated masses belong to the same component spectrum, a filtering algorithm is developed to exclude spectra of non-identical compounds. The basic ideas, methods, examples and experiences gained with applications are reported.     

Feature extraction from spectral and other data by the principal components and discriminant function techniques

Principal component analysis is applied to the interpretation of ¹³C-n.m.r. spectra and to the resolution of mass spectral data. A procedure is given for determining the relative amounts of pure components, with and without the use of pure mass lines, in mass spectra of mixtures. The use of the Fisher discriminant method in combination with the principal components technique is demonstrated in the treatment of trace element data on hair for environmental purposes. The importance of feature generation and selection is emphasized.     

Multicomponent analysis in luminescence spectroscopy

Multicomponent analysis of absorption spectra (infrared or electronic spectra) by factor analysis is a suitable method to use. The algorithm of factor analysis is now available for luminescence spectroscopy. The first possibility, as in absorption spectroscopy, is to determine pure components emission spectra for a unique set of spectra of mixtures in overdetermined conditions. On the other hand, it is also possible to extract pure components emission and excitation spectra of the only data corresponding to the 3D fluorescent spectrum of a single mixture. The results are evaluated in respect to noise level and concentrations ratio on both simulated and real spectra.     

Isolation,Characterization and Immunolocalization of a Seed Dominant CaM from Finger Millet (Eleusine coracana L. Gartn.) for Studying Its Functional Role in Differential Accumulation of Calcium in Developing Grains

To understand the exceptional high grain calcium accumulation in finger millet grains, a calmodulin (CaM) gene that is strongly expressed during developing spikes of high grain calcium genotype was further characterized. Using 5′–3′ RACE, the full-length CaM open reading frame (ORF) was isolated and the deduced protein sequence showed the presence of four characteristic EF motifs. Phylogenetic analysis showed that the finger millet CaM (Eleusine coracana calmodulin [EcCaM]) was identical to the rice CaM 1-1. Southern hybridization showed the presence of at least four copies of CaM gene that might be located on different regions of the finger millet "AABB" genome. Immunodetection using monospecific polyclonal anti-EcCaM antibodies revealed that EcCaM is localized in the embryo and aleurone layer and accumulates in higher amounts in high grain calcium genotype compared to the low grain calcium genotype. Furthermore, in silico analysis showed that EcCaM interacts with aquaporin which indicates that calcium is probably delivered to developing spike via mass flow of water. These results indicate that higher expression of CaM might cause greater stimulation of the downstream calcium transport machinery operative in the aleurone layer leading to the higher calcium accumulation in the grains of high grain calcium genotype.     

熵最小算法解析中药挥发油GC-MS谱中同分异构体的研究

采用以熵最小算法为基本原理的化学计量学方法,结合气相色谱-质谱联用分析法,对玫瑰精油及茉莉花挥发油中的2对同分异构体重叠峰进行重建分析,并分别得到各组分的重建质谱,同时采用NIST数据库相似度检索、质谱碎片信息比对及程序升温指数对分析得到的各组分进行定性分析。通过分析,分别从玫瑰精油和茉莉花挥发油的GC-MS重叠峰中分离得到1对同分异构体。分析结果显示,采用熵最小算法可以成功地对重叠峰中的各组分进行定性分析。该研究为类似同分异构体混合物中各组分的测定提供了参考。     

用逆向神经元模型从两维混和谱中提取纯组分两维光谱

提出一种少输入、多输出节点的逆向神经元模型.当训练样本数大于模型输入节点数时,可以得到唯一的神经元模型权重.所求得的权重是混合物中组分的纯光谱.将蒽、菲、芘混合物的荧光激发-发射光谱中提取的纯光谱与实验谱作了比较,并分别作为广义减秩法的校准预报未知混合物的浓度.结果表明所求得的谱比实验谱更适合于作校准.对于蒽、菲、芘、平均预报百分误差从7.0%、8.5%、7.8%分别下降到2.3%、5.0%、3.5%.     

Search for IR spectral features of less-abundant diisopropylnaphthalenes based on comparison of theoretical and experimental spectra

Experimental and theoretical B3PW91/6-31G* spectra of diisopropylnaphthalene (DIPN) were compared. For the 1,3- and 2,6-DIPN isomers, which were isolated as pure compounds, the theoretical IR spectra were scaled down and were shown to fit the experimental spectra very well. The same scaling factor was used for comparison theoretical and experimental spectra of isomers present in unresolved mixtures of isomers, i.e. 1,4-, 1,5-, 1,6-, 1,7-, and 2,7-DIPNs. For three isomers, 1,2-, 1,8-, and 2,3-DIPN, the experimental IR spectra, unknown so far, were predicted.     

An extension of the multivariate component-resolution method to three components

The extension of the multivariate curve resolution theory presented is based on the minimum assumptions of non-negativity of sensor responses and non-negativity of quantities of components, as given by Lawton and Sylvestre. The theory is explicitly given for three components, while the algorithms developed may potentially be extended to the more general n-component case. The analytical solution to the problem of defining the physically permitted regions for the sensor responses (“spectra”) or quantity profiles of pure components is given. Implementations of the algorithms developed are used for finding the permitted ranges of the pure component spectra from mixture spectra. Extensions of the minimal assumption theory are suggested.     

Fourier transform Raman spectral measurements of powdered quaternary mixtures of organic compounds: Exceptional pure component spectral reconstruction using band-target entropy minimization (BTEM)

Fourier transform Raman spectra of eight mixtures of four organic solids, namely dicyandiamide, melamine, acetamide and urea were measured. Matrices formed from these spectra were first subjected to singular value decomposition to obtain the right singular vectors. The right singular vectors were then subjected to blind source separation using band-target entropy minimization (BTEM), thus no a priori information (i.e. involving the nature of the components present, their spectra, nor their concentrations) was included in the analysis. The recovered pure component spectra are of exceptionally high quality and are consistent with pure reference spectra. Various empirical and statistical tests, such as the Euclidean norm and target transform factor analysis, were employed to assess the quality of the recovered spectra. The present results indicate the applicability of combined Raman and BTEM analysis for solid mixtures.     

Weighted two-band target entropy minimization for the reconstruction of pure component mass spectra: Simulation studies and the application to real systems

A method is proposed, on the basis of a recently developed algorithm--Band Target Entropy Minimization (BTEM)--to reconstruct mass spectra of pure components from mixture spectra. This method is particular useful in dealing with spectral data with discrete features (like mass spectra). Compared to the original BTEM, which has been applied to differentiable spectroscopies such as Fourier-transfer infrared spectroscopy (FTIR), ultraviolet (UV), Raman, and nuclear magnetic resonance (NMR), the latest modifications were obtained through: (1) Reformulating the objective function using the peak heights instead of their derivatives; (2) weighting the abstract vector VT to reduce the effect of noise; (3) using a two-peak targeting strategy (tBTEM) to deal with strongly overlapping peaks; and (4) using exhaustive search to locate all the component spectra. A set of 50 multi-component mass spectra was generated from ten reference experimental pure component spectra. Many of the compounds chosen have common MS fragments and therefore, many of the pure component spectra have considerable intensity in same data channels. In addition, a set of MS spectra from a real system with four components was used to examine the newly developed algorithm. Successful reconstruction of the ten component spectra of the simulated system and the four component spectra of the real system was rapidly achieved using the new tBTEM algorithm. The advantages of the new algorithm and its implication for rapid system identification of unknown mixtures are readily apparent.     

Mathematical algorithm for qualitative and semiquantitative analysis of petroleum hydrocarbons in solid wastes using on-line gas chromatography

Non-degradated mineral-oils like gasoline, solvent naphtha, diesel fuel, fuel and lubricating oils provide a characteristic fingerprint gas chromatogram. This visual classification, e.g. in solid wastes, is complicated due to the simultaneous presence of several mineral-oils. Therefore, a mathematical algorithm for the separation of gas chromatographic fingerprint of “single mixtures” of aliphatic hydrocarbons is developed. The technique ¶is essential for analysis of time-overlapping “single mixtures” of petroleum hydrocarbons (so-called “complex mixtures”) and it relies on the concentration-varying hydrocarbons during evaporation. It is possible to separate the data from the gas chromatogram of a “complex mixture” of hydrocarbons into the chromatograms of the pure “single mixtures” and to give their respective concentrations. A synthetic ?complex mixture” of kerosene, diesel fuel and lubricating oil is used to illustrate the method.     

Application of HPLC and Dual Wavelength Detection to the Analysis of Phenolic Compounds in Apples

Abstract

High-performance liquid chromatography, coupled with a programmable UV/visible detector, was applied to the identification and quantification of the phenolic compounds of apple tissue. The identity and purity of unknown components eluted from the HPLC column were established by comparing retention times and absorbance ratios with those of authentic compounds. In Golden Delicious apples, components with retention times agreeing with those of authentic catechin, epicatechin, chlorogenic acid, protocatechuic acid, p-hydroxybenzoic acid, p-coumaric acid, ferulic acid, and sinapic acid were found. However, comparison of absorbance ratios with those of authentic compounds suggested that many of the components that eluted in this system as single, symmetrical peaks were mixtures rather than individual compounds. This method helped to eliminate inaccurate identifications or faulty assumptions about homogeneity of HPLC “peaks”, errors that can occur when traditional HPLC methods that rely solely on detection at a single wavelength are employed. The method should have general applicability to plant extracts.     

含未知干扰物体系的多元光度分析的化学计量学算法

基于投影矩阵算法和最小熵概念,本文提出了处理含部份未知成份体系的定性定量分析新方法。将此法应用于金属离子及多环芳烃混合物体系测定,获得了满意的结果。     

Generation of substructure identification rules using feature-combinations from tandem mass spectra

Software to interpret tandem mass spectra, entitled Method for Analyzing Patterns in Spectra (MAPS), has been developed to provide substructure information for an automated compound identification system, This software consists of several program modules which manipulate databases of tandem mass spectra and substructure information, generate substructure identification rules, and apply these rules to the tandem mass spectra of unknown compounds to identify components of their structure. The MAPS rule generation program has been modified to generate rules based on specific combinations of spectral features that occur concertedly. False positives are drastically reduced by searching for “feature-combinations” that have 100% uniqueness with respect to a reference database of compounds. Recall is increased by the determination of multiple feature-combinations indicative of the presence of a given substructure. Strategies were developed in the algorithm for the discovery of feature-combinations that avoid the computation “explosion” that occurs when working with a large number of spectral features. The rules developed have the form: “IF feature-eombination a (FC a) or FC b,..., or FC x, THEN substructure SSn is present.”     

The binary system benzene—hexafluorobenzene studied by SSOZ theory and computer simulation. I. The charge symmetry model

The symmetry reduction of the SSOZ equation already developed for pure components is extended to binary mixtures. The general formalism is applied to the binary system C₆F₆/C₆H₆, whose “mixing interaction” is described by a simplified “charge symmetry model”. Varying the charge strength in a systematic way the predictions of SSOZ theory are verified by extensive computer simulation (molecular dynamics) studies. Thereby mixtures and pure components are treated in a unified way. Finally, extensions of the “charge symmetry model” are presented and the computed radial excess correlation functions are compared to those measured by neutron and X-ray scattering.     

Extraction of multiple pure component H and C NMR spectra from two mixtures: Novel solution obtained by sparse component analysis-based blind decomposition

Sparse component analysis (SCA) is demonstrated for blind extraction of three pure component spectra from only two measured mixed spectra in ¹³C and ¹H nuclear magnetic resonance (NMR) spectroscopy. This appears to be the first time to report such results and that is the first novelty of the paper. Presented concept is general and directly applicable to experimental scenarios that possibly would require use of more than two mixtures. However, it is important to emphasize that number of required mixtures is always less than number of components present in these mixtures. The second novelty is formulation of blind NMR spectra decomposition exploiting sparseness of the pure components in the wavelet basis defined by either Morlet or Mexican hat wavelet. This enabled accurate estimation of the concentration matrix and number of pure components by means of data clustering algorithm and pure components spectra by means of linear programming with constraints from both ¹H and ¹³C NMR experimental data. The third novelty is capability of proposed method to estimate number of pure components in demanding underdetermined blind source separation (uBSS) scenario. This is in contrast to majority of the BSS algorithms that assume this information to be known in advance. Presented results are important for the NMR spectroscopy-associated data analysis in pharmaceutical industry, medicine diagnostics and natural products research.