Identification of essential components of Houttuynia cordata by gas chromatography/mass spectrometry and the integrated chemometric approach

Starting with Biller-Biemann's work [J.E. Biller, K. Biemann, Anal. Lett. 7 (1974) 515], various kinds of approaches have been proposed to extract GC/MS data to obtain pure components responses. In this paper, an integrated chemometric approach is proposed, which combine four sequential steps, data pretreatment, component perception, resolution and component identification, and then the proposed approach is manipulated to analyze the essential oils of a herbal medicine named Houttuynia cordata (HC). On the basis of the selective information obtained from both chromatograms and mass spectra, the proposed integrated chemometric approach can resolve the two-way GC/MS responses matrix into pure chromatograms and mass spectra without any model assumption on the peak shape. The resolution results obtained from HC samples demonstrate the performance of the proposed approach and indicate that it may be a promising one for analyzing complex chromatograms.     

Deconvolution and quantitation of unresolved mixtures in liquid chromatographic-diode array detection using evolving factor analysis

Summary A recently developed self-modeling curve resolution method based in different factor analysis techniques has been applied for the first time to the study of liquid-chromatography-diode array data under situation where the separation of two components is not achieved. Two applications are reported: the resolution and quantitation of a coeluted mixture of carbamate pesticides pirimicarb and 1-naphthol, and the estimation of the concentration profiles of the double peak obtained in the elution of the triazine metabolite chlorodiamino-s-triazine. Different methods of quantitation are compared, including Evolving Factor Analysis and Rank annihilation. Quantitation from the area of the elution profiles once the component spectra have been transformed for their area contribution to the signal, gives a relative composition for pirimicarb and naphthol pesticides which agrees with the known sample composition. In the case of the unknown triazine mixture, an approximate quantitation of the two peaks obtained for this metabolite is obtained by assuming equal signal contribution or equal maximum absorbance of the individual spectra of the two detected components.     

Simultaneous determination of mixtures of nitrophenols using multivariate curve resolution-alternating least squares

A simple procedure for the quantitative determination of mixtures of nitrophenols pollutants in environmental and biological samples based on continuous spectrophotometric acid–base titrations and multivariate curve resolution has been proposed. The procedure simultaneously takes into account the spectroscopic and acid–base properties of the nitrophenols, which leads to a higher selectivity. The quantitative determination of analyte in the presence of unknown and uncalibrated interferences is a general benefit of the method. Moreover, only one single synthetic standard containing of the analyte of interest is enough to perform the analysis. Because of the rank-deficiency phenomena, the pure spectra and concentration profiles of the species of interest can only be recovered through matrix augmentation. Even in the presence of the rank deficiencies, in most cases accurate quantitation with relative errors in prediction lower than 5 % was obtained. The percent recoveries obtained (92–108 %) were satisfactory.     

Application of GRAM and TLD to the resolution and quantitation of real complex multicomponent mixtures by fluorescence spectroscopy

The application of the generalised rank annihilation method (GRAM) and the trilinear decomposition (TLD) method to the resolution and quantitation of fluorescence excitation-emission matrices of a ternary mixture of pesticides, carbendazim, fuberidazole, and thiabendazole, with overlapped spectra is described. The results obtained with both methods are compared and evaluated using measures of similarity (correlation coefficients) between the real and estimated spectra. Both approaches have been tested using augmented data matrices containing only two samples, but none of these methods succeeded completely in resolution of the system. When TLD was applied to augmented data matrices containing more than two samples better performance was achieved. To illustrate the application of both algorithms to real samples, they were used in the analysis of water samples containing the target pesticides. Again, TLD had an advantage over GRAM because the ability to analyse data from multiple (more than two) samples simultaneously allowed the resolution of the mixtures.     

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.     

Application of multivariate self-modeling curve resolution to the quantitation of trace levels of organophosphorus pesticides in natural waters from interlaboratory studies

Multivariate self-modeling curve resolution is applied to the quantitation of coeluted organophosphorus pesticides: fenitrothion, azinphos-ethyl, diazinon, fenthion and parathion-ethyl. Analysis of these pesticides at levels of 0.1 to 1 μg/l in the presence of natural interferences is achieved using automated on-line liquid-solid extraction (Prospekt) coupled to liquid chromatography and diode array detection followed by a recently developed multivariate self-modeling curve resolution method. The proposed approach uses only 100 ml of natural water sample and has improved resolution of the coeluted organophosphorus insecticides and their quantitation at trace level. The results have been compared with those obtained by different laboratories participating in the Aquacheck interlaboratory exercise (WRC, Medmenham, UK) where more conventional analytical techniques are being used.     

The analysis of mixtures: V

The work previously discussed has been extended. Product-moment correlation coefficients are shown to be useful in reducing the conditions hitherto necessary for the resolution of pure component spectra from an array of mixtures spectra. The method is illustrated by the solution of a five component calculated mixture in which there is only one unicomponent peak.     

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。     

Alternative calibration approaches for LC-MS quantitative determination of coeluted compounds in complex environmental mixtures using multivariate curve resolution

Different calibration approaches including external calibration, standard addition and internal standard are evaluated for quantification of coeluted compounds in liquid chromatography with MS spectrometry detection in scan mode and using multivariate curve resolution. These different calibration approaches are proposed to cope with sensitivity changes and matrix effects encountered in the analysis of complex natural environmental samples. By using them, multivariate curve resolution analysis of MS data in scan mode gave similar quantitative results to those obtained by LC-MS in selected ion monitoring (SIM) mode (in both cases errors were below 16% for internal standard combined with standard addition strategy), and it provided at the same time a means of analyte confirmation via their resolved pure MS spectra, and a means to gather a larger amount of information about the whole chromatographic process and to facilitate the simultaneous determination of multiple analytes in the same chromatographic run using the same experimental and instrumental conditions.     

Reduction of the rotational ambiguity of curve resolution techniques under partial knowledge of the factors. Complementarity and coupling theorems

Multivariate curve resolution techniques allow to uncover from a series of spectra (of a chemical reaction system) the underlying spectra of the pure components and the associated concentration profiles along the time axis. Usually, a range of feasible solutions exists because of the so‐called rotational ambiguity. Any additional information on the system should be utilized to reduce this ambiguity. Sometimes the pure component spectra of certain reactants or products are known, or the concentration profiles of the same or other species are available. This valuable information should be used in the computational procedure of a multivariate curve resolution technique. The aim of this paper is to show how such supplemental information on the components can be exploited. The knowledge of spectra leads to linear restrictions on the concentration profiles of the complementary species and vice versa. Further, affine–linear restrictions can be applied to pairs of a concentration profile and the associated spectrum of a species. These (affine) linear constraints can also be combined with the usual non‐negativity restrictions. These arguments can reduce the rotational ambiguity considerably. In special cases, it is possible to determine the unknown concentration profile or the spectrum of a species only from these constraints. Copyright © 2012 John Wiley & Sons, Ltd.     

Self-modeling curve resolution (SMCR) by particle swarm optimization (PSO)

Particle swarm optimization (PSO) combined with alternating least squares (ALS) is introduced to self-modeling curve resolution (SMCR) in this study for effective initial estimate. The proposed method aims to search concentration profiles or pure spectra which give the best resolution result by PSO. SMCR sometimes yields insufficient resolution results by getting trapped in a local minimum with poor initial estimates. The proposed method enables to reduce an undesirable effect of the local minimum in SMCR due to the advantages of PSO. Moreover, a new criterion based on global phase angle is also proposed for more effective performance of SMCR. It takes full advantage of data structure, that is to say, a sequential change with respect to a perturbation can be considered in SMCR with the criterion. To demonstrate its potential, SMCR by PSO is applied to concentration-dependent near-infrared (NIR) spectra of mixture solutions of oleic acid (OA) and ethanol. Its curve resolution performances are compared with SMCR with evolving factor analysis (EFA). The results show that SMCR by PSO yields significantly better curve resolution performances than those by EFA. It is revealed that SMCR by PSO is less sensitive to a local minimum in SMCR and it can be a new effective tool for curve resolution analysis.     

Dynamic kinetic transformation of sulfinyl chlorides: synthesis of enantiomerically pure C(2)-symmetric bis-sulfoxides.

Two modular and highly convergent approaches for the synthesis of both isomers of a large number of optically pure C(2)-symmetric bis-sulfoxides have been developed, and their scope and limitations have been assessed. The first one uses as intermediate diastereomerically pure C(2)-symmetric bis-sulfinate esters 6(S(S),S(S)) and 6(R(S),R(S)), obtained by dynamic kinetic resolution of ethane-1,2-bis-sulfinyl chloride 5. A single inducer of chirality, the glucose-derived DAG (diacetone-D-glucose) 1 is used for the enantioselective synthesis of both diastereomerically pure C(2)-symmetric bis-sulfinate esters, thanks to the opposite stereodirecting effect of pyridine and (i)Pr(2)NEt used to catalyze the reaction. The second approach is based on the copper-catalyzed oxidative coupling of optically pure lithiomethyl sulfoxides. Both isomers of a large number of methyl sulfoxides can be obtained in a convergent manner using (R(S))- and (S(S))-DAG methanesulfinate esters 8R(S) and 8S(S). Methanesulfinates 8R(S) and 8S(S) are also obtained in an enantioselective way by a dynamic kinetic resolution of methane sulfinyl chloride 24. The final bis-sulfoxides are obtained with enhanced enantioselectivities compared to the corresponding monomers, as a result of the Horeau effect which is operating in both approaches. A model based on the formation of pentacoordinated sulfur intermediate is proposed. This model can explain the dynamic kinetic resolution observed via Berry pseudorotations, without the commonly accepted in situ racemization of the starting material. The usefulness of the approaches is demonstrated by the preparation of complexes of Pd(II) and Ru(II) bearing bidentated chiral sulfoxides as ligands.     

Uniform Reduction of Scalar Coupling by Real‐Time Homonuclear J‐Downscaled NMR

Scalar coupling in proton NMR spectra provides important information for the structural analysis. However, the low resolution due to the resulting signal splitting, together with the rather narrow spectral range of hydrogen, often prevents the extraction of J‐coupling information. Here we present a method to achieve real‐time homonuclear J‐downscaling. Thereby, all J‐values are uniformly reduced by an arbitrary scaling factor. In the resulting one‐dimensional spectra, signal overlap is reduced, while scalar coupling information is still available.     

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.     

A comparative study of the correction of systematic errors in the quantitation of pyrethroids in vegetables using calibration curves prepared using standards in pure solvent

A comparative study of two mathematical approaches was performed in order to correct systematic errors due to the presence of the unexpected interferences which appear when the quantitation of the analyte in real samples is carried out with calibration curves built using standards in pure solvent. These methods consisted in the establishment of different mathematical expressions which transform the concentration (Cs) obtained using calibration graphs built using pure solvent into the corrected concentration (C(M)) that should be obtained if the quantitation is carried out with calibration curves built using standards dissolved in blank matrix extracts. In the two approaches the correction is performed from the results of an intermediate precision study which was carried out using both calibration graphs (prepared using pure solvent and blank matrix extract). By using ANCOVA to compare the slope of both solvent-based and matrix-matched calibration graphs, matrix effect was found in the determination of deltamethrin in tomato and acrinathrin in tomato and pepper. In these cases, both approaches led to good results.     

On the area of feasible solutions and its reduction by the complementarity theorem

Multivariate curve resolution techniques in chemometrics allow to uncover the pure component information of mixed spectroscopic data. However, the so-called rotational ambiguity is a difficult hurdle in solving this factorization problem. The aim of this paper is to combine two powerful methodological approaches in order to solve the factorization problem successfully. The first approach is the simultaneous representation of all feasible nonnegative solutions in the area of feasible solutions (AFS) and the second approach is the complementarity theorem. This theorem allows to formulate serious restrictions on the factors under partial knowledge of certain pure component spectra or pure component concentration profiles.     

Qualitative and Quantitative Analysis of Unresolved Responses In Liquid Chromatoraphy With Fourier Transform Infrared Spectroscopic Detection By Using the Kalman Filter

Abstract

The optimization of chromatographic separations is hindered by difficulties in establishing the identities of components, and by the need to have well-resolved chromatographic peaks for commonly-used methods of quantitation. Full spectra methods, such as Fourier transform infrared spectroscopy, may assist in establishing identities, but are hampered by poor sensitivity. A method is proposed which efficiently reduces the large amounts of spectral data obtained in such cases, and enhances signal-to-noise ratios to permit identification and quantitation of components present in well-and poorly-resolved chromatographic peaks obtained under nonoptimal conditions. the method is based on combined Gram-Schmidt orthogo-nalization for removal of the dominant response from the mobile phase, followed by Kalman filtering for qualitative and quantitative analysis of averaged spectra. the method is demonstrated for infrared detection of a reverse-phase separation of a five-component mixture. Although Kalman filtering is effective at enhancing weak     

Correction of retention time shifts for chromatographic fingerprints of herbal medicines

In this study, the combination of chemometric resolution and cubic spline data interpolation was investigated as a method to correct the retention time shifts for chromatographic fingerprints of herbal medicines obtained by high-performance liquid chromatography-diode array detection (HPLC-DAD). With the help of the resolution approaches in chemometrics, it was easy to identify the purity of chromatographic peak clusters and then resolve the two-dimensional response matrix into chromatograms and spectra of pure chemical components so as to select multiple mark compounds involved in chromatographic fingerprints. With these mark components determined, the retention time shifts of chromatographic fingerprints might be then corrected effectively. After this correction, the cubic spline interpolation technique was then used to reconstruct new chromatographic fingerprints. The results in this work showed that, the purity identification of the chromatographic peak clusters together with the resolution of overlapping peaks into pure chromatograms and spectra by means of chemometric approaches could provide the sufficient chromatographic and spectral information for selecting multiple mark compounds to correct the retention time shifts. The cubic spline data interpolation technique was user-friendly to the reconstruction of new chromatographic fingerprints with correction. The successful application to the simulated and real chromatographic fingerprints of two Cortex cinnamomi, fifty Rhizoma chuanxiong, ten Radix angelicae and seventeen Herba menthae samples from different sources demonstrated the reliability and applicability of the approach investigated in this work. Pattern recognition based on principal component analysis for identifying inhomogenity in chromatographic fingerprints from real herbal medicines could further interpret it.