Detection and use of selective ions for providing initial estimates of the concentration vectors in iterative target transformation factor analysis

The characteristic of discreteness in mass spectral direction distinguishes GC/MS from other spectral techniques. Based on this feature, we propose a new method to construct the initial concentration vectors for iterative target transformation factor analysis (ITTFA). For each chemical component, a search for a selective ion with good signal-to-noise ratio is first conducted using evolving factor analysis (EFA) and information entropy. The corresponding chromatogram of the selective ion is subsequently applied to construct an initial concentration vector for ITTFA. Special strategies are developed to cope with chromatographic patterns with embedded peaks and complex multicomponent structure. Results from three simulated and one real mixture and comparison with results from heuristic evolving latent projections (HELP) and a previously published method for definition of the initial profiles for ITTFA, indicate that selective ion chromatogram (SIC) ITTFA represents a fast, automatic and accurate method for resolution of GC/MS data.     

Resolution of multicomponent overlapped peaks A comparison of several curve resolution methods

Several curve resolution methods were compared in their performance of identification and quantification of overlapping peaks. These resolution methods are heuristic evolving latent projections (HELP), sub-window factor analysis (SFA), and orthogonal projection resolution (OPR). In this paper, HELP, SFA and OPR with the rank map produced by evolving factor analysis (EFA) or fixed size moving window-evolving factor analysis (FSMW-EFA) were applied to the simulated and experimental data. The resolved results were compared qualitatively and quantitatively. In addition, the further comparison was carried out with the results obtained from multivariate curve resolution-alternate least square (MCR-ALS) by using the initial estimates provided by EFA.     

Application of multivariate curve resolution methods to on-flow LC-NMR

The application of evolving window factor analysis (EFA), subwindow factor analysis (SFA), iterative target transformation factor analysis (ITTFA), alternating least squares (ALS), Gentle, automatic window factor analysis (AUTOWFA) and constrained key variable regression (CKVR) to resolve on-flow LC-NMR data of eight compounds into individual concentration and spectral profiles is described. CKVR has been reviewed critically and modifications are suggested to obtain improved results. A comparison is made between three single variable selection methods namely, orthogonal projection approach (OPA), simple-to-use interactive self-modelling mixture analysis approach (SIMPLISMA) and simplified Borgen method (SBM). It is demonstrated that LC-NMR data can be resolved if NMR peak cluster information is utilised.     

A novel approach of initial vectors construction in iterative target transformation factor analysis

The construction of the favorable initial iterative vectors is the key to iterative target transformation factor analysis (ITTFA). A tentative approach to construct the better initial vectors, which is based on the chromatographic information provided by evolving factor analysis (EFA), is proposed. A region, which contains the peak position at maximum height, is determined. Several elements in the region of each initial vector, instead of one element, are initialized as 1. The elements out of the region are initialized as 0. So it is not necessary to determine the exact peak position at maximum height for the resolution of partly overlapping chromatographic profiles, which may avoid the divergence brought by determination of the peak position at maximum height. In addition, it may give acceptable resolution for the embedded peaks. It is applied to resolve 2D-simulated data and experimental liquor GC/MS data, the resolutions are reasonable and improved.     

Multivariate curve resolution combined with gas chromatography to enhance analytical separation in complex samples: A review

This review describes the major advantages and pitfalls of iterative and non-iterative multivariate curve resolution (MCR) methods combined with gas chromatography (GC) data using literature published since 2000 and highlighting the most important combinations of GC coupled to mass spectrometry (GC–MS) and comprehensive two-dimensional gas chromatography with flame ionization detection (GC × GC-FID) and coupled to mass spectrometry (GC × GC–MS). In addition, a brief summary of some pre-processing strategies will be discussed to correct common issues in GC, such as retention time shifts and baseline/background contributions. Additionally, algorithms such as evolving factor analysis (EFA), heuristic evolving latent projection (HELP), subwindow factor analysis (SFA), multivariate curve resolution-alternating least squares (MCR-ALS), positive matrix factorization (PMF), iterative target transformation factor analysis (ITTFA) and orthogonal projection resolution (OPR) will be described in this paper. Even more, examples of applications to food chemistry, lipidomics and medicinal chemistry, as well as in essential oil research, will be shown. Lastly, a brief illustration of the MCR method hierarchy will also be presented.     

Resolving co-eluting chromatographic patterns by means of dissimilarity analysis in iterative target transformation factor analysis

The initialization of concentration vector for iterative target transformation factor analysis (ITTFA) and identification of pure or key variables are the important issue in MCR. In this study, dissimilarity analysis and evolving factor analysis (EFA) are combined to find the selective or key variables and subsequently obtain initial estimates of the concentration vectors for resolution of gas chromatography/mass spectrometry (GC/MS) data by ITTFA. For systems containing components with highly similar mass spectra, a new constraint setting the elements out of elution window to 0 is used to improve convergence rate and accuracy of results. Tested by standard mixture of two wax esters and real GC/MS data of gasoline 97#, dissimilarity based ITTFA could obtain accurate results (average Dot product of concentration vectors, average deviation of peak area ratio and average similarity of mass spectra are 0.9929, 0.0228 and 981.0, respectively).     

二维重叠色谱峰解析方法的比较

利用渐进因子分析方法(EFA)和固定尺寸移动窗口因子分析方法(FSMWEFA)与局部正交投影方法(LOPA)相结合,用于模拟的HPLC—DAD二维数据解析。通过比较谱峰部分重叠、光谱完全重叠、色谱拖尾3种情况的结果显示,在谱峰部分重叠和色谱拖尾两种情况下两种方法都可以较好的解析重叠峰。但是在光谱完全重叠时,前一种方法不能得出正确的结论,而后一种方法仍能得到满意的结果。     

Application of Multivariate curve resolution-alternating least square methods on the resolution of overlapping CE peaks from different separation conditions

Discussed in this paper is the development of a new strategy to improve resolution of overlapping CE peaks by using second-order multivariate curve resolution with alternating least square (second-order MCR-ALS) methods. Several kinds of organic reagents are added, respectively, in buffers and sets of overlapping peaks with different separations are obtained. Augmented matrix is formed by the corresponding matrices of the overlapping peaks and is then analyzed by the second-order MCR-ALS method in order to use all data information to improve the precision of the resolution. Similarity between the resolved unit spectrum and the true one is used to assess the quality of the solutions provided by the above method. 3,4-Dihydropyrimidin-2-one derivatives (DHPOs) are used as model components and mixed artificially in order to obtain overlapping peaks. Three different impurity levels, 100, 20, and 10% relative to the main component, are used. With this strategy, the concentration profiles and spectra of impurities, which are no more than 10% of the main component, can be resolved from the overlapping peaks without pure standards participant in the analysis. The effects of the changes in the components spectra in the buffer with different organic reagents on the resolution are also evaluated, which are slight and can thus be ignored in the analysis. Individual data matrices (two-way data) are also analyzed by using MCR-ALS and heuristic evolving latent projections (HELP) methods and their results are compared with those when MCR-ALS is applied to augmented data matrix (three-way data) analysis.     

多元曲线分辨-交替最小二乘方法在联用数据分辨中的应用

通过对模拟数据和高效毛细管电泳实验数据的分析,讨论了多元曲线分辨-交替最小二乘方法(MCR-ALS)在毛细管电泳-二极管阵列检测(CE-DAD)联用数据分辨中的应用.讨论了几种因素对MCR-ALS单个数据矩阵分辨结果的影响,包括待分析物光谱间的相似程度、浓度曲线的重叠程度以及由渐进因子分析(EFA)所得到的浓度初始值等.MCR-ALS还可用于多个数据矩阵的同时分析,即二阶MCR-ALS.结果表明,与一阶MCR-ALS相比,二阶MCR-ALS方法能够更好地解决各种分辨问题,得到合理和满意的分辨结果.     

Investigation of the acid-base properties of mononitro-calix[4]arene with chemometric methods

The acid-base properties of mononitro-calix[4]arene was studied with chemometric methods by measurement of its UV absorbance under different pH. The chemometric method-iterative target transformation factor (ITTFA) was employed to resolve the acid-base fraction curves. Combining with other chemometric methods-principal component analysis (PCA) and evolving factor analysis (EFA), the proton dissociation behavior of the derivative was investigated in detail. The pK(a) values of the derivative were determined and the fraction curves and pure absorbing spectra of each absorbing component were obtained.     

Resolution of overlapping capillary electrophoresis peaks by using chemometric analysis: quantification of the components in compound reserpine tablets

The application of multivariate curve resolution with alternating least squares (MCR-ALS) methods to second-order data from capillary electrophoresis with diode array detector (CE-DAD) is reported. Initial qualitative solutions obtained by evolving factor analysis (EFA) and pure-variable detection method can be further optimized by a simultaneous analysis of multiple electrophoresis run data with ALS regression. While unknown samples are analyzed simultaneously against the corresponding standards in different composition ratios, the exact amounts of common components in different CE runs can be determined by the traditional calibration curve method, and quantification can thus be achieved. The above methods are applied to the determination of the components in compound reserpine tablets in overlapping peaks from CE. The quantification results are compared with those of the first derivative of the electropherogram method and artificial neural network (ANN) method.     

气相色谱/质谱结合化学计量学解析方法用于植物精油成分分析

使用气相色谱/质谱(GC-MS)法对缬草精油和含有缬草精油的混配精油的化学成分进行了分析.采用直观推导式演进特征投影法(HELP)对二维数据中的色谱重叠峰进行了解析,得到两种精油的各个物质的纯色谱和纯质谱,同时利用交互移动窗口因子分析法(AMWFA)直接比较两种精油中的共有组分,提取出共有组分的纯质谱,通过与HELP法解析出的质谱进行比较,发现AMWFA法比HELP法能更快速鉴别出混配精油中的缬草精油.     

Simultaneous polarographic analysis of pyrazine and its methyl derivatives by iterative target transformation factor analysis

The polarographic waves of pyrazine and its methyl derivatives are seriously overlapping, so they cannot be determined individually by polarographic methods without a prior separation. In this paper, a chemometric approach, iterative target transformation factor analysis (ITTFA), is developed and applied to the determination of mixtures of pyrazines at trace level (2.0–9.0 × 10⁻⁶moll⁻¹) by using differential pulse polarography (DPP) and a static mercury drop electrode (SMDE). Different from the general ITTFA method, only one-dimensional measurement data of n − 1 standards and an unknown were used in this work. It produced acceptable results with average recoveries in the 96–108% range and relative standard errors in the 3.4–9.5% range.     

Combined kinetics and iterative target transformation factor analysis for spectroscopic monitoring of reactions

Obtaining rate constants and concentration profiles from spectroscopy is important in reaction monitoring. In this paper, we combined kinetic equations and Iterative Target Transformation Factor Analysis (ITTFA) to resolve spectroscopic data acquired during the course of a reaction. This approach is based on the fact that ITTFA needs a first guess (test vectors) of the parameters that will be estimated (target vectors). Three methods are compared. In the first, originally proposed by Furusj? and Danielsson, kinetic modelling is only used to provide the initial test vectors for ITTFA. In the second the rate constant used to provide the test vectors is optimised until a best fit is reached. In the third, a guess of the rate constant is used to provide the test vectors to ITTFA. The outcome of ITTFA is then used to fit the kinetic model and obtain a new guess of the rate constant. With this constant new concentration profiles are generated and provided to the ITTFA algorithm as new test vectors, in an iterative manner, minimising the residuals of the predicted dataset, until convergence. The second and third methods are new implementations of ITTFA and are compared to the first, established, method. First order (both one and two step) and second order reactions were simulated and instrumental noise was introduced. An experimental second order reaction was also employed to test the methods.     

Study on multimers and their structures in molecular association mixture

Self-association system of(R)-1,3-butanediol in dilute carbon tetrachloride(CCl4)solution is studied as a model of molecular association mixture.Analysis methods including FSMWEFA(fixed-size moving window evolving factor analysis)combined with PCA(principal component analysis),SIMPLISMA (simple-to-use interactive self-modeling mixture analysis),and ITTFA(iterative target transformation factor analysis)are adopted to resolve infrared spectra of(R)-1,3-butanediol solution.Association number and equilibrium constant are computed.(R)-1,3-butanediol in dilute inert solution is determined as a monomer-trimer equilibrium system.Theoretical investigation of trimer structures is carried out with DFT(density functional theory),and structural factors are analyzed.     

直观推导式演进特征投影法测定不同方法提取的国产血竭中龙血素B的含量

利用HPLC-DAD产生的二维数据和化学计量学方法-直观推导式演进特征投影法（HELP），解析了不同提取方法得到的国产血竭中的重叠色谱峰，并对其中的指标性成分-龙血素B进行了含量测定，结果满意。这表明这化学计量学方法与现代分析手段有机结合，将为中药等复杂体系的分析提供一条新途径。     

Simultaneous mixture analysis by using non-linear spectrophotometric data and linear iterative target transformation factor analysis

Non-linear absorption spectral data obtained from ternary mixtures of analytes are analyzed by using a linear model, iterative target transformation factor analysis (ITTFA). The use of transformed original variables is used to correct non-linearities in the original data. Absorbance below a certain limit (k) is described as linear and above this limit as non-linear. The extension of the regressor variables is the squared absorbances above the linear range. The variation of the prediction error as a function of the number of the factors and the k-values were considered and the minimum prediction error was evaluated for reaching to optimum. Except the natural non-negativity constraint the correlation constraint also is used on concentration vector in each iteration of ITTFA algorithm. The reliability of the method is evaluated using model data for ternary mixtures by spectral overlapping and different degrees of non-linearity. Simultaneous spectrophotometric determination of Eu³⁺, UO₂²⁺ and Th⁴⁺ with arsenazo III as chromogenic reagent is used as experimental model systems with non-linearity behavior of Eu³⁺and UO₂²⁺ components. The application to both synthetic and real data sets with different degrees of non-linearity demonstrate the ability of the proposed methodology to obtain better results than original data and ITTFA. The relative standard errors of prediction for proposed method in comparison with using the PLS calibration on original and extended data are nearly smaller.     

Analysis of PAHs in air-borne particulates in Hong Kong City by heuristic evolving latent projections

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Towards obtaining more information from gas chromatography–mass spectrometric data of essential oils: An overview of mean field independent component analysis

Mean field independent component analysis (MF-ICA) along with other chemometric techniques was proposed for obtaining more information from multi-component gas chromatographic–mass spectrometric (GC–MS) signals of essential oils (mandarin and lemon as examples). Using these techniques, some fundamental problems during the GC–MS analysis of essential oils such as varying baseline, presence of different types of noise and co-elution have been solved. The parameters affecting MF-ICA algorithm were screened using a 2⁵ factorial design. The optimum conditions for MF-ICA algorithm were followed by deconvolution of complex GC–MS peak clusters. The number of independent components (ICs) (chemical constituents) in each peak cluster was estimated using morphological score method. Eigenvalue profiles of evolving factor analysis (EFA) and pure variables from orthogonal projection approach (OPA) were used as initial mixing matrix (chromatograms) in iterative process. The resolved mass spectra were satisfactorily identified using NIST mass spectral search system. Finally, the results of optimized MF-ICA were compared with those obtained using multivariate curve resolution-alternating least square (MCR-ALS), multivariate curve resolution-objective function minimization (MCR-FMIN) and heuristic evolving latent projection (HELP) methods. It is demonstrated that MF-ICA can be used as an alternative method for a quick and accurate analysis of real multi-component problematic systems such as essential oils.     

Characterization of essential oil components of Iranian geranium oil using gas chromatography-mass spectrometry combined with chemometric resolution techniques

The essential oil components of geranium oil cultivated in center of Iran were identified and determined using gas chromatography-mass spectrometry data combined with the chemometric resolution techniques. A total of 61 components accounting for 91.51% were identified using similarity searches between the mass spectra and MS database. This number was extended to 85 components using chemometric techniques. Various chemometric methods such as morphological scores, simplified Borgen method (SBM) and fixed size moving window evolving factor analysis (FSMWEFA) were used for determining the number of components, pure variables, zero concentration and selective regions. Then the overlapping peak clusters were resolved into pure chromatograms and pure mass spectra using heuristic evolving latent projections (HELP) method. A characteristic feature of the Iranian geranium oil is the absence of 10-epi-gamma-eudesmol in its constituents compared with the oil from northern and southern parts of India. The results of this work show that combination of hyphenated chromatographic methods and resolution techniques provide a complementary method for accurate analysis of essential oils.