Rapid authentication of variants of Gastrodia elata Blume using near-infrared spectroscopy combined with chemometric methods.

The variety is one of the most important factors to generate difference of chemical compositions, which unavoidably influences the quality of natural medicine. Thus, simple and rapid authentication of different variants has great academic and practical significance. In this study, the goal was achieved with the help of near infrared spectroscopy (NIR) and chemometrics by using Gastrodia elata Blume as an example. A total of 540 samples including two classes of variants and their forms were investigated as a whole. The mean spectra of samples of each class and their 2-D synchronous correlation spectra were simultaneously applied to discover the difference of chemical characteristics. After hybrid pre-processing of the first and second derivative combined with Savitzky-Golay and Norris filtering, partial least squares discrimination analysis (PLS-DA) on the basis of latent variable projection was used to assess the feasibility for classification. The results show higher prediction accuracy in both internal test set and external prediction set. In order to further improve the robustness for modeling, three methods for wavelength selection were comprehensively compared to optimize PLS-DA models, including variable importance in the projection (VIP), random frog (RF), and Monte Carlo uninformative variable elimination (MC-UVE). The prediction accuracy of combination of the 2nd derivative, Norris, MC-UVE and PLS-DA achieved to 99.11% and 98.89% corresponding to the internal test set and external prediction set, respectively. The strategies proposed in this work perform effectiveness for rapid and accurate authentication of variants of plants with high chemical complexity.

Semiautomated Alignment of High-Throughput Metabolite Profiles with Chemometric Tools.

The rapid increase in the use of metabolite profiling/fingerprinting techniques to resolve complicated issues in metabolomics has stimulated demand for data processing techniques, such as alignment, to extract detailed information. In this study, a new and automated method was developed to correct the retention time shift of high-dimensional and high-throughput data sets. Information from the target chromatographic profiles was used to determine the standard profile as a reference for alignment. A novel, piecewise data partition strategy was applied for the determination of the target components in the standard profile as markers for alignment. An automated target search (ATS) method was proposed to find the exact retention times of the selected targets in other profiles for alignment. The linear interpolation technique (LIT) was employed to align the profiles prior to pattern recognition, comprehensive comparison analysis, and other data processing steps. In total, 94 metabolite profiles of ginseng were studied, including the most volatile secondary metabolites. The method used in this article could be an essential step in the extraction of information from high-throughput data acquired in the study of systems biology, metabolomics, and biomarker discovery.

A modeling approach for orthogonality of comprehensive two-dimensional separations.

A novel method is developed for orthogonality evaluation of comprehensive two-dimensional separations (C2DS). Utilization of efficiency measures such as peak capacity (n(c)) can be critically evaluated for C2DS analysis to describe an orthogonal separation of the analytes in a 2D plane. Unlike most previous methods focusing on "bin coverage" over 2D space, rather than taking into account the distribution based on accurate peak retention, in the proposed method, the separation orthogonality of C2DS is divided into two parts (i.e., C(pert) and C(peaks)). These correspond to peak coverage percent, and 2D distribution correlation of compounds, respectively. Bin occupation and a simple-linear regression model, on the basis of normalized retention times in 2D separation space ((1)t(R) and (2)t(R)), are further introduced to quantitatively define the two terms. Orthogonality ranges from 0 to 1 correspond to perfectly correlated and orthogonal separations, respectively, which are presented based on both C(pert) and C(peaks) considerations. The advantage of this method is the use of separation properties of C2DS to characterize practical 2D peak distribution and does not rely on assumptions or any imposed limitations. Simulation of comprehensive two-dimensional gas chromatography (GC × GC) was achieved by using the Abraham solvation parameter model, and applied to generate examples for orthogonality assessment. In this work, 225 compounds comprising a range of chemical classes were simulated for separation on two column set pairs comprising low polarity/polar and moderately polar/polar combinations. Results illustrate that the proposed method applied to GC × GC provides a reasonable assessment of 2D separation performance and may be used to derive optimal experimental conditions when used with an experimental design strategy.

Simultaneous estimation of retention times of overlapping primary peaks in comprehensive two-dimensional GC.

A general approach is developed to estimate the retention time (t(R)) of overlapping primary peaks in comprehensive 2D GC without assumptions such as the limitation of modulation ratio or symmetry of the target primary peak. Accurate determination of t(R) can recover the original peak profile by using mathematical fitting. First, the modulation pattern of the first-dimension peak in the second dimension is summarized with the number of major modulation fractions equal to or less than four. A novel formula to derive t(R) is defined and separately investigated for cases of modulation number for 1-4, on the basis of the center of gravity of all fractions of the primary peak, in the second dimension. A moving-window search strategy is further developed for peak clusters overlapping in both separation dimensions, to extract first dimension tR of pure components with the same second dimension time after data pretreatment (background subtraction; time shift correction). Results are very close to the times of simulated comprehensive 2D GC separations. Compared to methods such as reconstruction of original chromatographic profiles to derive t(R), the proposed approach balances the precision, and ease of adaption to real applications.

Component correlation between related samples by using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry with chemometric tools.

A chemometric strategy has been developed to discover component difference and similarity between two chromatograms (correlation) by using comprehensive two-dimensional (2D) gas chromatography with time-of-flight mass spectrometry (GC×GC-TOFMS). It allows for rapid determination of the presence or absence of analytes of interest in both pure and overlapping peak clusters, and then locates elution windows of target components. First, representative elution windows of analytes are extracted from the 2D GC×GC map to characterize the spectral space and further construct an orthogonal projection matrix for analysis. Next, multi-component spectral correlative chromatography (MSCC) is employed to scan the whole or pre-selected GC×GC-TOFMS data range to obtain component features. An auto-correlative projection curve is proposed to assess the projection residual from MSCC by defining a new evaluation index as reference, based on fixed-size moving window evolving factor analysis. In principle, the method can also be utilized to locate specific compounds whose known spectra are available. It is not restricted by data with high homoscedastic and heteroscedastic noise. Simulated GC-MS data and an extremely complicated herbal product mixture comprising 9 herbs demonstrates that the two-dimensional correlative distribution graph is effective for chemical interpretation between GC×GC-TOFMS data. It allows discovery of information buried in this type of highly complex dataset, especially for rapid and effective data comparison, where specific molecular identity might otherwise be hidden.

Chemometrics in comprehensive multidimensional separations.

Chemometric methods have critical importance for the discovery of the information/knowledge buried or concealed in high-dimensional datasets acquired from comprehensive multidimensional separations (CMDS), and for interpretation of experiments or chemical processes. In this work, employment of new developments in chemometrics making full use of the data to maximize the potential of CMDS to resolve mathematically a variety of practical problems is reviewed whilst providing the authors' point of view. During the past several years, chemometrics has been successfully applied to many areas of concern to CMDS investigation, including experimental parameter optimization, data quality improvement, identification and quantification of target chemical components, pattern recognition technique for clustering and classification, multivariate model establishment to correlate chromatographic properties and molecular descriptors. On the basis of the high-dimensionality characteristics of CMDS, some special aspects such as evaluation of orthogonality and image processing have also been included in this review. It is expected that an overview of the diverse ways in which chemometrics can aid CMDS investigations will prove valuable to interested users in this area through a comprehensive survey of previous research contributions. Chemometrics lends itself well to the powerful separation capability of CMDS.

Simultaneous deconvolution and re-construction of primary and secondary overlapping peak clusters in comprehensive two-dimensional gas chromatography.

In this study, simultaneous deconvolution and reconstruction of peak profiles in the first ((1)D) and second dimension ((2)D) of comprehensive two-dimensional (2D) gas chromatography (GC×GC) is achieved on the basis of the property of this new type of instrumental data. First, selective information, where only one component contributes to the peak elution window of a given modulation event, is employed for stepwise stripping of each (2)D peak with the help of pure components corresponding to that compound from the neighbouring modulations. Simulation based on an exponentially modified Gaussian (EMG) model aids this process, where the EMG represents the envelope of all (2)D peaks for that compound. The peak parameters can be restricted by knowledge of the pure modulated (2)D GC peaks derived from the same primary compound, since it is modulated into several fractions during the trapping and re-focusing process of the cryogenic modulation system according to the modulation period. Next, relative areas of all pure (2)D components of that compound are considered for reconstruction of the primary peak. This strategy of exploitation of the additional information provided by the second dimension of separation allows effective deconvolution of GC×GC datasets. Non-linear least squares curve fitting (NLLSCF) allows the resolved 2D chromatograms to be recovered. Accurate acquisition of the pure profiles in both (1)D and (2)D aids quantification of compositions and prediction of 2D retention parameters, which are of interest for qualitative and quantitative analysis. The ratio between the sum of squares of deconvolution residual and original peak response (R(rr)) is employed as an effective index to evaluate the resolution results. In this work, simulated and experimental examples are used to develop and test the proposed approach. Satisfactory performance for these studies is validated by minimum and maximum R(rr) values of 1.34e-7% and 1.09e-2%; and 1.0e-3% and 3.0e-1% for deconvolution of (1)D and (2)D peaks, respectively. Results suggest that the present technique is suitable for GC×GC data processing.

Comparative qualitative analysis of nonylphenol isomers by gas chromatography-mass spectrometry combined with chemometric resolution.

The relationship between nonylphenol (NP) isomers' structures and their estrogenic potencies has been evaluated previously. However, due to their similarities in both chemical and physical properties, complete separation and identification remain strikingly difficult. In the present study, gas chromatography-mass spectrometry (GC-MS) is employed to separate commercial NP isomers. Both extracted ion chromatograms (EIC) based on selected ions known to be definitive for the suite of isomers, and the heuristic evolving latent projection (HELP) chemometric resolution method have been applied for the analysis and identification of the NP isomers. This method corrected the wrong identification of one isomer which was suspected based on the EIC data, and also was able to be applied for the determination of an additional isomer with low abundance. Overall, 15 NP isomers have been proposed by the HELP interpretation method. Pure component chromatograms and mass spectra have been extracted with the aid of chemometric resolution. The applicability of the commercial deconvolution software package automated mass spectral deconvolution and identification system (AMDIS) has also been tested against the HELP method for comparative presentation of pure component mass spectra.

Quantification of target components in complex mixtures using alternative moving window factor analysis and two-step iterative constraint method.

Alternative moving window factor analysis (AMWFA) has shown the powerfulness for comprehensive comparison and individual identification of chemical components among different but related mixture systems. However, quantification of these components can only be attained after extraction of all spectra of pure components in samples with least square technique. In this study, a novel two-step iterative constraint method (TICM) is developed for independent quantification of the interested target analytes. The pure chromatographic profiles of the components can be mined out from mixtures with high complexity using a two-step iterative operation and stepwise purification of the targets from interferers. Some effective constraints of chromatographic profiles, such as non-negative and single-peaked properties, as well as zero-concentration outside of elution windows of components, are employed to further improve the efficiency of the method. One of the strong advantages of TICM is simplification of complex mixtures to several sub-systems for processing easily with the help of AMWFA, as well as bi-linear property of data sets obtained from coupled chromatographic instruments. It meets the urgent requirements and challenges of qualitative and quantitative analysis of complicated systems with multi-component in the investigation of herbal medicines (HMs), metabonomics and systems biology. From the results of simulated LC-DAD data, GC-MS data of volatile chemical components in three kinds of ginseng with different growth conditions, and four different medicinal parts of the same herb, good performance of the proposed method is achieved.

Mass spectral profiling: an effective tool for quality control of herbal medicines.

Quality control of herbal medicines (HMs) is a big big headache because of the high complexity and unknown mechanism on disease treatment. In this work, mass spectral profiling, a new tool for data processing is proposed to help a lot in solving this problem as gas chromatography-mass spectroscopy (GC-MS) is used to detect both the active and non-active ingredients buried in HMs. The main idea of mass spectral profiling is employment of target m/z points of GC-MS data on the extraction of chromatographic profiles of pure and/or mixed compositions concerned. Further, the absolute or relative abundance at these m/z points can be utilized for results interpretation. With the help of this tool, the qualitative and quantitative information of chemical components within complicated HMs will be mined out effectively. It can then be recommended as reference indices to assess the importance of target compositions in HMs, such as efficacy evaluation on disease treatment of the active constituents. Mass spectral profiling with less data points significantly improves the possibility to get the rich information with no strong requirements of data preprocessing procedures, like alignment of shift of retention times among different chromatographic profiles. It is powerful for quality control of HMs coupled with pattern recognition techniques on high-throughput data sets. In this study, a commonly used herbal medicine, Houttuynia cordata Thunb and its finished injection products, were used to deliver the strategies. Absolutely, the working principles can be extended to the investigation of metabonomics with gas chromatography-time-of-flight-mass spectrometry (GC-MS-TOF). The good performance of mass spectral profiling shows that it can be a promising tool in the future studies of complex mixture systems.

Comparative analysis of volatile components from Clematis species growing in China.

The volatile components between stems and roots and also among five Clematis species from China were studied and analyzed by gas chromatography-mass spectrometry (GC-MS) combined with alternative moving window factor analysis (AMWFA), a new chemometric resolution method. Identification of the compounds was also assisted by comparison of temperature-programmed retention indices (PTRIs) on HP-5MS with authentic samples included in our own laboratory database under construction. A total of 153 different compounds accounting for 86.6-96.5% were identified and significant qualitative and quantitative differences were observed among the samples. The major volatile components in different essential oils from Clematis species were n-hexadecanoic acid and (Z,Z)-9,12-octadecadienoic acid. Our work further demonstrated chemometric resolution techniques upon the two-dimensional data and PTRIs can provide a complementary and convenient method for fast and accurate analysis of complex essential oils.

Orthogonal projection (OP) technique applied to pattern recognition of fingerprints of the herbal medicine houttuynia cordata Thunb. and its final injection products.

It is a crucial issue to determine the origins of herbal medicinal materials and identify the quality grades and fakes of their final products collected from different pharmaceutical corporations. Pattern recognition technique may assist the manufacturers to achieve this purpose and effectively control the quality of their products. In this work, a widely used method in chemometrics, orthogonal projection (OP) technique, was applied to discrimination analysis and identification of fingerprints of the herbal medicine houttuynia cordata Thunb. (HCT) and its final injection products. The advantages of the OP technique are clearly shown after comparing with the conventional methods such as principal component analysis (PCA), Mahalanobis distance (MD), and similarity comparison method (SCM). Three different sources of medicinal material HCT and its final injection products from six different manufacturers were studied under 'sixfold', 'threefold' and 'threefold-bis' cross-validation procedures. The good performance of the proposed method in determination and identification of unknown samples shows it could be a powerful tool for quality control in herbal medicine production and other related research fields.

Alternative moving window factor analysis for comparison analysis between complex chromatographic data.

In this investigation, a novel chemometric method is developed for the analysis of five possible relationships of components or spectral features between two correlative but different hyphenated chromatographic systems. It is very helpful for comparison study of components present in different complex systems in both chemistry and systems biology. The proposed method, named alternative moving window factor analysis (AMWFA), could be utilized to determine the number of common components between different samples and then to identify their corresponding spectra half-automatically. AMWFA can alternatively be employed to mind for the selective information hiding in anyone of the two compared data X and Y, and to self-verify the resolution results by changing the extracted target matrices in analysis. From the results of comparison of simulated hyphenated chromatographic data, volatile chemical components in drug pair rhizoma ligustici chuanxiong-radix paeoniae rubra (RLC-RPR) and its single herbal medicines, and analysis of Angelica oral solution and its plasma sample after oral intake to rabbit, powerful ability of the proposed method is shown.

Comparing chemical fingerprints of herbal medicines using modified window target-testing factor analysis.

A "chromatographic fingerprint" of a herbal medicine is essentially its chromatographic spectrum: a characteristic representation of its chemical components, some of which are pharmacologically active. Since a wide variety of factors, such as the geographical location, the harvest season, and the part used can influence the chemical constituents (and therefore the pharmacological activity) of any particular herbal medicine and its products, these fingerprints provide a way to compare and contrast the compositions of different variants of the same herbal medicine. In particular, it is possible to ascertain whether particular components present in one herbal fingerprint are also present in another fingerprint. In this work we use a novel method-modified window target-testing factor analysis (MWTTFA), based on the use of target factor analysis (TFA), fixed-size moving window evolving factor analysis (FSMWEFA) and a Gaussian shape correction to the chromatographic profiles-to achieve this end. To demostrate the strategy, the fingerprints of samples from garlics produced in different geographical locations were compared, as well as the fingerprints of samples taken from above-ground and below-ground parts of Houttuynia cordata Thunb. The results from these comparisons clearly show that four chemical components present in Hunan common edible garlic are absent in Xingping base garlic, while seven components are present in Xingping base garlic but absent in Hunan common edible garlic. Also, eleven components are present in the sample from the above-ground part of Houttuynia cordata Thunb but not in the sample from the below-ground part, while seven components are present in the sample from the below-ground part of Houttuynia cordata Thunb that are not present in the sample from the above-ground part. These interesting conclusions should be very useful for future pharmacological and clinical research into these herbal medicines, and the novel MWTTFA technique can also be used for quality control purposes.

External factor variable connectivity index.

A new variable index, external factor variable connectivity index (EFVCI), is proposed, in which the atomic attribute is divided into two parts. The innate part is denoted as outer-shell electrons and external part or perturbation by other atoms is represented as summation, multiplied by a variable x, of squared reciprocal matrix of i row (corresponds to atom A(i)). The division of atomic attribute in EFVCI is interpreted by using topological structure. In the correlation of boiling point of 149 acyclic alkanes, the optimal values will approach to a constant at -0.29 by using the zero to higher order indices of the same series. The new index, with high regression quality (R = 0.9986, s = 2.26, and F = 7088.4), is compared favorably with variable connectivity index and molecular connectivity index.