Enhanced spectrum matching of mixed mass spectra by means of quality factors

A system of computer programs for recognizing impure or mixed spectra was assembled that provided automatic subtraction of reference mass spectra from a mixed spectrum. The software was 99% successful in recognizing pure spectra for the cases tested. Subtractions were done on mixed spectra in 70% of the cases tested. Spectrum subtraction enhanced the ability of the matching program to match components of a multicomponent mixture correctly, whereas quality factors were a great aid in the evaluation of the overall match validity.     

Data handling in HPLC-diode array UV-spectrometry

Summary The mathematical resolution of overlapped chromatographic peaks obtained in HPLC with a diode array UV detector has received considerable attention recently. The purpose of the proposed methods is the quantitation and identification of unknown solutes in complex mixtures by an efficient use of all available data. Basically two approaches have been proposed: the first one resolves the concentration profiles and calculates the pure spectra by applying a minimal number of assumptions, which is denoted self-modeling-curve resolution. The second one is based on a match of pure spectra available in a spectral library with the measured mixture spectra. In this paper both approaches are evaluated with respect to their performance to provide the pure spectra and an accurate quantitation of the concentrations.     

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.     

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.     

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.     

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.     

Prediction of viscosities of hydrocarbon mixtures

A new model for prediction of the viscosities of hydrocarbons including oil and gas mixtures is presented. The model is based on the principle of corresponding states with methane and decane as reference components. The viscosity of a given component or mixture is determined from the reduced viscosities of the reference components using the molecular weight as an interpolation parameter.

The model has been used for prediction of viscosities of both pure components and mixtures over large pressure ranges and for reduced temperatures above 0.476. The results are in good agreement with the experimental data. The new model compares favorably with earlier published methods, which use only one reference component.

Finally, the model has been tested on data for 6 oil mixtures from the North Sea. The mean deviation based on 34 experimental points was 6.4 %.     

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%.     

A new approach to near-infrared spectral data analysis using independent component analysis

This paper presents a new approach to near-infrared spectral (NIR) data analysis that is based on independent component analysis (ICA). The main advantage of the new method is that it is able to separate the spectra of the constituent components from the spectra of their mixtures. The separation is a blind operation, since the constituent components of mixtures can be unknown. The ICA based method is therefore particularly useful in identifying the unknown components in a mixture as well as in estimating their concentrations. The approach is introduced by reference to case studies and compared to other techniques for NIR analysis including principal component regression (PCR), multiple linear regression (MLR), and partial least squares (PLS) as well as Fourier and wavelet transforms.     

目标识别因子分析解析完全未知混合体系的研究

本文以目标识别因子分析为基础, 将因子分析与聚类分析相结合, 给出了从完全未知混合体系中提取纯物种光谱的新方法。所得纯物种光谱经光谱检索作定性判别, 然后利用外标法对混合物中的各组份进行标定。该方法用于计算机模拟体系及三组份实际混合体系的红外光谱解析, 结果令人满意。     

Similarity search for a 1H-NMR spectroscopic data base

A similarity-search system is described for proton-NMR spectroscopy. In order to achieve fast retrieval of reference compounds, the ¹H-NMR spectra of the data base and of the unknown are encoded in a bitsring. The individual bits of the binary signature describe different features of the spectra. Part of the coupling information is coded in such a way that effects of magnetic field strength are taken into account. The encoding thus permits a fast search for identical and structurally similar reference compounds in the data base even when the spectra were recorded at different magnetic field strengths. Because the search consists of weighted comparison of bits, each of them describing different spectral features, a choice of different kinds of searches is possible with the same signature by selecting appropriate weight vectors. Thus specific spectroscopic features can be selected for the search. Such a context-sensitive similarity-search system allows, for example, a search for compounds having similar multiplicities or similar subspectra in a given (e.g., aromatic) region of the spectrum. Furthermore, by adjusting two “software knobs” which influence the normalization of the search results, the user can choose between the two extremes of forward and reverse search, and between an identity search, similarity search or classification search. The results were tested on a small library containing 550 spectra including some mixtures and duplicates recorded under different experimental conditions at 250 and 400 MHz.     

Automatic analysis of mixed spectra : Generalised spectral subtraction applied to electron spin resonance spectroscopy

A new technique is described for estimating the pure component spectra from a set of linearly independent spectra. The process is one of generalised spectral subtraction in which an iterative combination of multivariate linear least-squares analysis and matrix transformation is applied to the input data to give estimates of the number of independent components in the original mixed spectra. This technique is applicable to bipolar data (e.g. from e.p.r. spectra) as well as absorption spectra determined by any spectroscopic technique, provided that the spectra may be reasonably assumed to be an additive mixture of unknown pure components. Numerical model examples are given together with an experimental application to electron spin resonance.     

Computer controlled micropreparative isolation and enrichment from GC capillary columns and subsequent sample handling

This paper describes a computerized system for micropreparative enrichment of components from a mixture being separated on a capillary column. All electronic components are commercially available. Programming was performed in BASIC so that users can easily adapt the program to their own special needs. The enrichment procedure is based on a repetitive absorption of eluting compounds after separation from a capillary column, by sucking them through an absorption tube mounted at the outlet splitter. In order to perform this procedure reliably, the system searches for reference peaks which can stem from componetns of the mixture or from deliberately added reference compounds. Use of error diagnostics prevents contamination of the collected fractions in case of system failures. Optimization is easily performed during a supervised GC run in a semiautomatic mode. Using the function keys of the computer, the operator can adjust injection volume, measure the base line drift at any retention time, and perform reference peak allocation and peak switching until optimal conditions have been found. The system presented has been in operation for several years in our laboratory where, in some cases, the mixture had to be injected onto the capillary column over 500 times to obtain the amount of pure component (10–1000 μg) necessary for NMR structure elucidation. However, recording of meaningful spectra of such small amounts requires special precautions in sample handling. This technique can also be applied for sample recovery after analysis by non-destructive elucidation methods or by chemical microreactions without loss.     

Chemometric resolution of ATR-IR spectra data for polycondensation reaction of bis(hydroxyethylterephthalate) with a combination of self-modeling curve resolution (SMCR) and local rank analysis

Self-modeling curve resolution (SMCR) methods, simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) and alternating least squares (ALS) were used to calculate pure concentration profiles and pure spectra for the two-way spectral data collected during the on-line polycondensation reaction of bis(hydroxyethylterephthalate) with an ATR-FT-IR spectrometer. In order to improve the resolution results, SIMPLISMA was combined with local rank analysis method, fixed size moving window evolving factor analysis (FSMWEFA) to search for selective regions of various components and then look for the purest wavenumber variables in the selective regions. Such combination allows more accurate determination of the number of chemical components in the reaction system and the calculations of more accurate concentration profiles and spectra.     

Mutual peak matching in a series of HPLC–DAD mixture analyses

One of the largest challenges in high performance liquid chromatography (HPLC) method development is the necessity for tracking the movement of peaks as separation conditions are changed. Peak increments are then used to build a mathematical model capable of minimizing the number of experiments in an optimization circuit. Method optimization for an unknown mixture is, moreover, complicated by the absence of any a priori information on component properties and retention times when direct signal assignment is not possible. On the contrary, achievement of the maximum separation becomes an important factor for successful identification or quantitation. In this case, the optimization may be based on assigning peaks of the same component chosen from different experiments to each other. In other words, mutual peak matching between the HPLC runs is required.

A new method for mutual peak matching in a series of HPLC with diode array detector (HPLC–DAD) analyses of the same unknown mixture acquired at varying separation conditions has been developed. This approach, called mutual automated peak matching (MAP), does not require any prior knowledge of the mixture composition. Applying abstract factor analysis (AFA) and iterative key set factor analysis (IKSFA) on the augmented data matrix, the algorithm detects the number of mixture components and calculates the retention times of every individual compound in each of the input chromatograms. Every candidate component is then validated by target testing for presence in each HPLC run to provide quantitative criteria for the detection of “missing” peaks and non-analyte components as well as confirming successful matches. The matching algorithm by itself does not perform full curve resolution. However, its output may serve as a good initial estimate for further modeling. A common set of UV-Vis spectra of pure components can be obtained, as well as their corresponding concentration profiles in separate runs, by means of alternating least-square multivariate curve resolution (ALS MCR), resulting in reconstruction of overlapped peaks.

The algorithms were programmed in MATLAB^® and tested on a number of sets of simulated data. Possible ways to improve the stability of results, reduce calculation time, and minimize operator interaction are discussed. The technique can be used to optimize HPLC analysis of a complex mixture without preliminary identification of its components.     

Spectrophotometric determination of mixtures of 2-, 3-, and 4-hydroxybenzaldehydes by flow injection analysis and uv/vis photodiode-array detection

A multivariate approach to the quantitative determination of 2-hydroxybenzaldehyde (salicylaldehyde), 3-hydroxybenzaldehyde, and 4-hydroxybenzaldehyde in their mixtures is described. The method is based on second order data generated in a flow injection analysis system with a pH gradient and photodiode-array detection. Each injection gave rise to an 89 (times) x 101 (wavelength) matrix, containing both the acidic and the basic characteristics of the sample injected. A least-squares algorithm based on Lambert-Beers law was used for the prediction of concentrations in unknown samples. No assumptions concerning the qualitative mixture composition of the hydroxybenzaldehydes were necessary to perform concentration predictions. The following four data types were used in the least-squares modelling: (1) unfolded raw data, (2) acidic spectra, (3) basic spectra, and (4) first spectral derivative of the raw data. The prediction errors obtained were comparable to literature results. A graphic method, based on the model residuals for detecting erroneous samples, was developed.     

Independent component analysis applied to diffusion‐ordered spectroscopy: separating nuclear magnetic resonance spectra of analytes in mixtures

Nuclear magnetic resonance spectrum of a mixture contains the overall peaks of all the analytes. It is impossible to perform structural assignment on the mixture without the knowledge of individual spectra of the components. Spectral separation is thus an important means of teasing out pure components of a mixture before spectral assignment. We propose a strategy called diffusion‐ordered independent component analysis (DIFFICA) to achieve this task. This strategy applies independent component analysis algorithms to diffusion‐ordered spectroscopy (DOSY) to extract spectra of pure components in a mixture. DIFFICA was tested in a simulation and experimentally in two three‐component systems with and without water suppression, in 1D and 2D DOSY data. Pure spectra were achieved in both cases. The selection of diffusion parameters to guarantee pure spectra is guided by the distance correlation between separated spectra. Copyright © 2012 John Wiley & Sons, Ltd.     

化学演讲过程数据的自动分辨: 复杂石油样品的GC－MS分析

基于几种新化学计量学方法,提出了可自动解析化学演进数据的新途径。在该法中,首先选择关键向量。通过子空间比较法确定数据的化学秩。以关键向量为起点,通过初等变换不断迭代而实现对数据的解析。并利用数据非负性等检验解析结果的可靠程度。结合GC－MS数据的特点,选择的关键变量可为关键质谱或关键浓度曲线。该方法能极大地减少人为干预,大大降低数据分析时间,对文中的石油样本共解析出557个成分。     

Testing the performance of pure spectrum resolution from Raman hyperspectral images of differently manufactured pharmaceutical tablets

Chemical imaging is a rapidly emerging analytical method in pharmaceutical technology. Due to the numerous chemometric solutions available, characterization of pharmaceutical samples with unknown components present has also become possible. This study compares the performance of current state-of-the-art curve resolution methods (multivariate curve resolution-alternating least squares, positive matrix factorization, simplex identification via split augmented Lagrangian and self-modelling mixture analysis) in the estimation of pure component spectra from Raman maps of differently manufactured pharmaceutical tablets. The batches of different technologies differ in the homogeneity level of the active ingredient, thus, the curve resolution methods are tested under different conditions. An empirical approach is shown to determine the number of components present in a sample. The chemometric algorithms are compared regarding the number of detected components, the quality of the resolved spectra and the accuracy of scores (spectral concentrations) compared to those calculated with classical least squares, using the true pure component (reference) spectra. It is demonstrated that using appropriate multivariate methods, Raman chemical imaging can be a useful tool in the non-invasive characterization of unknown (e.g. illegal or counterfeit) pharmaceutical products.