An automatic LC-MS/MS data analysis workflow for herbal compound annotation with AutoAnnotatoR: A case study of ten botanical origins of Fritillaria species.

Background: Despite the widespread implementation of analytical hardware capable of recording large-scale datasets for botanical natural products, the data processing procedures for compound annotation remain a bothersome obstacle that demand a tremendous amount of time and expert knowledge.

Methods: Herein, an automatic LC-MS/MS data analysis workflow with AutoAnnotatoR was introduced for the compound annotation of plant derived natural products, which has the merits of great efficiency, high accuracy, saving time and simplified process. This procedure enabled automatic matching of MS data with characteristic fragment ions, as well as MS data with compound libraries, which improves the accuracy of structural elucidation.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.