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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. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicines (TCMs) bring a great challenge in quality control and evaluating the efficacy because of their complexity of chemical composition. Chemometric techniques provide a good opportunity for mining more useful chemical information from TCMs. Then, the application of chemometrics in the field of TCMs is spontaneous and necessary. This review focuses on the recent various important chemometrics tools for chromatographic fingerprinting, including peak alignment information features, baseline correction and applications of chemometrics in metabolomics and modernization of TCMs, including authentication and evaluation of the quality of TCMs, evaluating the efficacy of TCMs and essence of TCM syndrome. In the conclusions, the general trends and some recommendations for improving chromatographic metabolomics data analysis are provided. [ABSTRACT FROM AUTHOR]

Metabolomics datasets generated by modern analytical instruments tend to be increasingly complex. In this study, a recent method named shrunken centroids regularized discriminant analysis (SCRDA) has been introduced and applied in the exploration of metabolomics dataset. It is a supervised method for variable selection, discriminant analysis and biomarker screening. By regularizing the estimate of the within-class covariance matrix, SCRDA can deal with the singularity issue of linear discriminant analysis. Then a shrinkage estimator is applied to perform variable selection. The method presented is illustrated through the simulated datasets and three complex metabolomics datasets. Commonly used orthogonal partial least squares discriminant analysis and two other similar statistical methods, penalized linear discriminant analysis and nearest shrunken centroids, are used for comparisons. The results illustrate that SCRDA has some desirable abilities in variable selection, classification and prediction. Moreover, the biomarkers identified by SCRDA are further demonstrated to be in accordance with the biochemical research. It has been proved that SCRDA can be applied as a promising strategy in metabolomics. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Model-population analysis (MPA) was recently proposed as a general framework for designing new types of chemometrics and bioinformatics algorithms, and it has found promising applications in chemistry and biology. The goal of MPA is to extract useful information from complex analytical systems, so as to lead to better understanding and better modeling of chemical and biological data. To give an overall picture of MPA, we first review its key elements. Then, we describe the theories and the applications of selected methods that focus on the two fundamental aspects in chemical and biological modeling: outlier detection and variable selection. We highlight the key common principles of these methods and pinpoint the critical differences underlying each method. [Copyright &y& Elsevier]

Abstract: Chromatography has been extensively applied in many fields, such as metabolomics and quality control of herbal medicines. Preprocessing, especially peak alignment, is a time-consuming task prior to the extraction of useful information from the datasets by chemometrics and statistics. To accurately and rapidly align shift peaks among one-dimensional chromatograms, multiscale peak alignment (MSPA) is presented in this research. Peaks of each chromatogram were detected based on continuous wavelet transform (CWT) and aligned against a reference chromatogram from large to small scale gradually, and the aligning procedure is accelerated by fast Fourier transform cross correlation. The presented method was compared with two widely used alignment methods on chromatographic dataset, which demonstrates that MSPA can preserve the shapes of peaks and has an excellent speed during alignment. Furthermore, MSPA method is robust and not sensitive to noise and baseline. MSPA was implemented and is available at http://code.google.com/p/mspa. [Copyright &y& Elsevier]

Abstract: Kernel methods, in particular support vector machines, have been further extended into a new class of methods, which could effectively solve nonlinear problems in chemistry by using simple linear transformation. In fact, the kernel function used in kernel methods might be regarded as a general protocol to deal with nonlinear data in chemistry. In this paper, the basic idea and modularity of kernel methods, together with some simple examples, are discussed in detail to give an in-depth understanding for kernel methods. Three key ingredients of kernel methods, namely dual form, nonlinear mapping and kernel function, provide a consistent framework of kernel-based algorithms. The modularity of kernel methods allows linear algorithms to combine with any kernel function. Thus, some commonly used chemometric algorithms are easily extended to their kernel versions. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Recently, the fingerprinting approach using chromatography has become one of the most potent tools for quality assessment of herbal medicine. Due to the complexity of the chromatographic fingerprint and the irreproducibility of chromatographic instruments and experimental conditions, several chemometric approaches such as variance analysis, peak alignment, correlation analysis, and pattern recognition were employed to deal with the chromatographic fingerprint in this work. To facilitate the data preprocessing, a software named Computer Aided Similarity Evaluation (CASE) was also developed. All programs of chemometric algorithms for CASE were coded in MATLAB5.3 based on Windows. Data loading, removing, cutting, smoothing, compressing, background and retention time shift correction, normalization, peak identification and matching, variation determination of common peaks/regions, similarity comparison, sample classification, and other data processes associated with the chromatographic fingerprint were investigated in this software. The case study of high pressure liquid chromatographic HPLC fingerprints of 50 Rhizoma chuanxiong samples from different sources demonstrated that the chemometric approaches investigated in this work were reliable and user friendly for data preprocessing of chromatographic fingerprints of herbal medicines for quality assessment. [ABSTRACT FROM AUTHOR]

Abstract: A total of 95 volatile compounds from the essential oil in buds of Syringa oblata Lindl (lilac) were identified by gas chromatography–mass spectrometry (GC–MS) combined with heuristic evolving latent projections (HELP) and moving subwindow searching (MSS). The identified compounds are mainly aliphatic, terpenes and aromatic compounds. Their temperature-programmed retention indices (PTRIs) on HP-5MS and DB-35MS at three heating rates of 2, 4 and 6°C/min from 80 to 290°C were obtained, which showed that aliphatic compounds give nearly constant PTRIs and PTRIs of terpenoids do not vary much at different heating rates. But PTRIs of aromatic compounds exhibit relatively large temperature dependence. PTRIs vary much more on DB-35MS than those on HP-5MS according to the compound types. In general, differences of PTRIs between the two columns increase from aliphatic compounds to terpenoids to polycyclic aromatic compounds. The PTRIs in different heating rates were used as cross-references in the identification of components in the essential oil. When they were used in analysis of essential oil from flowers of lilac, good results were obtained. These PTRIs would be a part of our PTRI database being constructed on components from plant essential oils. The results also showed that efficiency and reliability were improved greatly when chemometric method and PTRIs were used as assistants of GC–MS in identification of chemical components in plant essential oils. [Copyright &y& Elsevier]

Abstract: Different chromatographic and electrophoretic techniques commonly used in the instrumental inspection of herbal medicines (HM) are first comprehensively reviewed. Chemical fingerprints obtained by chromatographic and electrophoretic techniques, especially by hyphenated chromatographies, are strongly recommended for the purpose of quality control of herbal medicines, since they might represent appropriately the “chemical integrities” of the herbal medicines and therefore be used for authentication and identification of the herbal products. Based on the conception of phytoequivalence, the chromatographic fingerprints of herbal medicines could be utilized for addressing the problem of quality control of herbal medicines. Several novel chemometric methods for evaluating the fingerprints of herbal products, such as the method based on information theory, similarity estimation, chemical pattern recognition, spectral correlative chromatogram (SCC), multivariate resolution, etc. are discussed in detail with examples, which showed that the combination of chromatographic fingerprints of herbal medicines and the chemometric evaluation might be a powerful tool for quality control of herbal products. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

A chemometric resolution method is described for the identification of nitrogen compounds in crude oil. Prefractionation of crude oil into discrete chemical classes was performed by adsorption column chromatography using small quantities of neutral aluminum oxide and silicic acid. Subsequent high-resolution separation of individual components was achieved by using capillary column gas chromatography, and compound types were detected by mass spectrometer. In conjunction with a combined chemometric method, each principal chemical class was further resolved and separated, which made it possible to identify some nitrogen compounds in the investigated oils. To a certain extent, this method could relieve classical analysis of difficulty in identifying those species with poorly low contents or partially chromatographic overlaps, particularly in the cases where authentic standards were not available for addition into the unknown analytical systems to reveal what indeed existed in them. [Copyright &y& Elsevier]

The qualitative and quantitative determination of the chemical constitutes in traditional Chinese medicine (TCM) is an important task, which builds the foundation of the theory of pharmacological activity. The hyphenated chromatography instruments combined with the related chemometric methods provide powerful tools for the resolution of such complex systems. The familiar chemometrics methods can be roughly divided into two different kinds, the iterative one such as orthogonal projection approach (OPA) and non-iterative one representing by evolving window orthogonal projection (EWOP). One can use different kinds of methods according to overlapping condition, and then the measured data matrix can be resolved into pure concentration profiles and mass spectra of the chemical components with relative high efficiency and acceptable accuracy. One kind of TCM, named Notoptergium incium (NI) was analyzed by gas chromatography–mass spectrometry (GC–MS) and resolved by above chemometric approach. Experiment results show the efficiency and convenience of the proposed approach. 65 of the 98 separated constituents in essential oil, accounting for 92.13%, were identified by mass spectroscopy (MS). [Copyright &y& Elsevier]

An evolving approach is described for analyzing the acidic components of a petroleum ether extract of Radix Rehmanniae Preparata. The latter was extracted, separated and derivatized. The subsequent separation of the individual components was characterized by GC-MS. The two-way data were resolved into a pure chromatogram and a mass spectrum of each chemical component. The reliability and accuracy of the qualitative results were greatly improved and the degree of the chromatographic separation could be enhanced to a certain extent. This enabled the analysis of complicated practical systems by hyphenated instruments and these advanced methods. [ABSTRACT FROM AUTHOR]

In order to improve detection ability and quality of resolution of overlapping peaks with low signal-to-noise (SNR) ratio data obtained from GC/MS, the effect of heteroscedastic noise is investigated in the present paper. A new index named smoothing distortion (SD) is first developed for evaluating the smoothing efficiency. Roughness penalty smoothing method recently appearing in chemometrics is then compared with wavelet denoising technique and convolution smoothing approach under condition of heteroscedastic noise. The performance of the methods is assessed using both simulated and experimental GC/MS data. The results obtained show that the roughness penalty method cannot only enhance the detection ability but also improve quality of resolved chromatographic profiles and spectra significantly for the noisy GC/MS data. [Copyright &y& Elsevier]

Model population analysis (MPA) is a general framework for designing new types of chemometrics algorithms that has attracted increasing interest in the chemometrics community in recent years. The goal of MPA is to extract statistical information from the model, towards better understanding of the chemical data. Two key elements of MPA are random sampling and statistical analysis. The core idea of MPA is quite universal with potential applications in the fields, such as chemoinformatics, biostatistics and bioinformatics. In this article, we review the development of MPA in chemometrics. We first present the key elements of MPA. Then, the application of MPA in chemometrics is discussed, such as variable selection, model evaluation, outlier detection, applicability domain definition and so on. Finally, the potential application areas of MPA in future research are prospected. [ABSTRACT FROM AUTHOR]

Abstract: Large amounts of data from high-throughput analytical instruments have generally become more and more complex, bringing a number of challenges to statistical modeling. To understand complex data further, new statistically-efficient approaches are urgently needed to: [(1)] select salient features from the data; [(2)] discard uninformative data; [(3)] detect outlying samples in data; [(4)] visualize existing patterns of the data; [(5)] improve the prediction accuracy of the data; and, finally, [(6)] feed back to the analyst understandable summaries of information from the data. We review current developments in tree-based ensemble methods to mine effectively the knowledge hidden in chemical and biology data. We report on applications of these algorithms to variable selection, outlier detection, supervised pattern analysis, cluster analysis, and tree-based kernel and ensemble learning. Through this report, we wish to inspire chemists to take greater interest in decision trees and to obtain greater benefits from using the tree-based ensemble techniques. [Copyright &y& Elsevier]

Abstract: Retention time shifts badly impair qualitative or quantitative results of chemometric analyses when entire chromatographic data are used. Hence, chromatograms should be aligned to perform further analysis. Being inspired and motivated by this purpose, a practical and handy peak alignment method (alignDE) is proposed, implemented in this research for one-way chromatograms, which basically consists of five steps: (1) chromatogram lengths equalization using linear interpolation; (2) accurate peak pattern matching by continuous wavelet transform (CWT) with the Mexican Hat and Haar wavelets as its mother wavelets; (3) flexible baseline fitting utilizing penalized least squares; (4) peak clustering when gap of two peaks is smaller than a certain threshold; (5) peak alignment using differential evolution (DE) to maximize linear correlation coefficient between reference signal and signal to be aligned. This method is demonstrated with both simulated chromatograms and real chromatograms, for example, chromatograms of fungal extracts and Red Peony Root obtained by HPLC-DAD. It is implemented in R language and available as open source software to a broad range of chromatograph users (http://code.google.com/p/alignde). [ABSTRACT FROM AUTHOR]

Abstract: In the structure–activity relationship (SAR) study, a learning algorithm is usually faced with the problem of selecting a compact subset of descriptors related to the property of interest, while ignoring the rest. This paper presents a new method of molecular descriptor selection utilizing three commonly used decision tree (DT)-based ensemble methods coupled with a backward elimination strategy (BES). Our proposed method eliminates descriptor redundancy automatically and searches for more compact descriptor subset tailored to DT-based ensemble methods. Six real SAR datasets related to different categorical bioactivities of compounds are used to evaluate the proposed method. The results obtained in this study indicate that DT-based ensemble methods coupled with BES, especially boosting tree model, yield better classification performance for compounds related to ADMET. [Copyright &y& Elsevier]

Abstract: Chemometric Modeling Markup Language (CMML) is developed by us for containing chemometrics models within one document through converting binary data into strings by base64 encode/decode algorithms to solve the interoperability issue in sharing chemometrics models. It provides a base functionality for storage of sampling, variable selection, pretreating, outlier and modeling parameters and data. With the help of base64 algorithm, the usability of CMML is in equilibrium with size by transforming the binary data into base64 encoded string. Due to the advantages of Extensible Markup Language (XML), models stored in CMML can be easily reused in various other software and programming languages as long as the programming language has XML parsing library. One can also use the XML Path Language (XPath) query language to select desired data from the CMML file effectively. The application of this language in near infrared spectroscopy model storage is implemented as a class in C++ language and available as open source software (http://code.google.com/p/cmml), and the implementations in other languages, such as MATLAB and R are in progress. [Copyright &y& Elsevier]

Abstract: Tangerine peels are herbal materials of two coupled traditional Chinese medicines, Pericarpium Citri Reticulatae (PCR) and Pericarpium Citri Reticulatae Viride (PCRV). In this paper, high-performance liquid chromatographic fingerprints of tangerine peels during growth were firstly measured for deliberately collected 34 samples from three species (Citrus reticulata ‘Chachi’, Citrus reticulata ‘Dahongpao’ and Citrus erythrosa Tanaka). After sixteen characteristic components which have similar change trends in the grown process were screened out with the help of heuristic evolving latent projection (HELP) method, score plots of principal component analysis (PCA) successfully presented the grown footprints of tangerine peels. It implied that July might be the best harvest time for PCRV, November and December were better for PCR. Furthermore, hesperidin, nobiletin and tangeretin were screened as chemical markers by loadings of PCA. The HPLC–HELP–PCA strategy has shown its potential in optimization of harvest time and chemical markers’ screening, which will have wide perspective in the analysis of “coupled TCMs”. [Copyright &y& Elsevier]

Abstract: Essential oils from four samples of Rhododendron were extracted by water distillation and analyzed by gas chromatography–mass spectrometry with the help of retention indices and chemometrics resolution method named subwindow factor analysis (SFA). A total of 128 volatile components were identified reliably and fleetly. A temperature-programmed retention index (I u T) dataset including these components has been constructed on a slightly polar capillary column (HP-5MS) at the given GC operating condition in which the standard GC parameter S = r T t M / β =0.0087. The present work proved the usefulness of chemometrics and retention indices in complicated systems analysis and the I u T''s obtained can be used in other essential oil identification. The major components of the analyzed samples showed that essential oils from different genus or even different parts of azaleas are different from each other in chemical components. [Copyright &y& Elsevier]

Abstract: Boosting is one of the most important recent developments in classification methodology. It can significantly improve the prediction performance of any single classification algorithm and has been successfully applied to many different fields including problems in chemometrics. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data, and then taking a weighted majority vote of the sequence of classifiers thus produced. In this paper, we proposed a generalized boosting algorithm via Bayes optimal decision rule. Using Bayes optimal decision rule, we adjust the weights of the sequence of classifiers in the voting process of boosting algorithm. The two types of errors are introduced into the generalized boosting and make the voting process more sensible. Meanwhile, the weights of the training samples are also correspondingly adjusted according to some criterion. The generalized boosting is applied to the binary classification for chemical data. Experimental results show that it can improve the predict accuracy compared with AdaBoost algorithm especially when the difference between the two types of errors for classification is large. [Copyright &y& Elsevier]

One of the main tasks of chemometrics is to classify chemical objects to one of several distinct predefined categories. There are many classification methods in data mining, one of which is the boosting technique that can improve predicate performance of a given classifier and it is one of the most powerful methods in classification methodology. In this paper, we apply boosting neural network (NN) and boosting tree in classification for chemical data. Experimental results show that boosting can significantly improve the prediction performance of any single classification method. Two techniques to interpret the model are also introduced in order to help us better understand the experimental results. [Copyright &y& Elsevier]

Partial least squares (PLS) and principal component regression (PCR) are two widely used techniques for dimension reduction in chemometrics. However, the relationship between PLS and PCR is not entirely understood. In this paper, we introduce the idea of sufficient dimension reduction (SDR) to chemometrics, and show that PLS and PCR are methods of SDR. Furthermore, this paper shows that these two methods are equivalent within the framework of SDR which means that there is no theoretical advantage of PLS over PCR in terms of prediction performance. The above conclusion is supported by the results of a simulated dataset and three real datasets. [ABSTRACT FROM AUTHOR]

A method for quantitative analysis of the polysaccharides contents in Glycyrrhiza was developed based on near infrared (NIR) spectroscopy, and by adopting the phenol–sulphuric acid method as the reference method. This is the first time to use this method for predicting polysaccharides contents in Glycyrrhiza . To improve the predictive ability (or robustness) of the model, the competitive adaptive reweighted sampling (CARS) mathematical strategy was used for selecting relevance wavelengths. By using the restricted relevance wavelengths, the PLS model was more efficient and parsimonious. The coefficient of determination of prediction ( R p 2 ) and the root mean square error of prediction (RMSEP) of the obtained optimum models were 0.9119 and 0.4350 for polysaccharides. The selected relevance wavelengths were also interpreted. It proved that all the wavelengths selected by CARS were related to functional groups of polysaccharide. The overall results show that NIR spectroscopy combined with chemometrics can be efficiently utilised for analysis of polysaccharides contents in Glycyrrhiza. [ABSTRACT FROM AUTHOR]

Renal interstitial fibrosis closely relates to chronic kidney disease and is regarded as the final common pathway in most cases of end-stage renal disease. Metabolomic biomarkers can facilitate early diagnosis and allow better understanding of the pathogenesis underlying renal fibrosis. Gas chromatography–mass spectrometry (GC/MS) is one of the most promising techniques for identification of metabolites. However, the existence of the background, baseline offset, and overlapping peaks makes accurate identification of the metabolites unachievable. In this study, GC/MS coupled with chemometric methods was successfully developed to accurately identify and seek metabolic biomarkers for rats with renal fibrosis. By using these methods, seventy-six metabolites from rat serum were accurately identified and five metabolites (i.e., urea, ornithine, citric acid, galactose, and cholesterol) may be useful as potential biomarkers for renal fibrosis [ABSTRACT FROM AUTHOR]

In this work, a novel strategy based on chromatographic fingerprints and some chemometric techniques is proposed for quantitative analysis of the formulated complex system. Here, the formulated complex system means a formulated type of complicated analytical system containing more than one kind of raw material under some concentration composition according to a certain formula. The strategy is elaborated by an example of quantitative determination of mixtures consist of three essential oils. Three key steps of the strategy are as follows: (1) remove baselines of the chromatograms; (2) align retention time; (3) conduct quantitative analysis using multivariate regression with entire chromatographic profiles. Through the determination of concentration compositions of nine mixtures arranged by uniform design, the feasibility of the proposed strategy is validated and the factors that influence the quantitative result are also discussed. This strategy is proved to be viable and the validation indicates that quantitative result obtained using this strategy mainly depends on the efficiency of the alignment method as well as chromatographic peak shape of the chromatograms. Previously, chromatographic fingerprints were only used for identification and/or recognition of some products. This work demonstrates that with the assistance of some effective chemometric techniques, chromatographic fingerprints are also potential and promising in solving quantitative problems of complex analytical systems. [ABSTRACT FROM AUTHOR]

Abstract: Support vector machine (SVM) has been gaining popularity in the field of chemistry. However, it also suffered from the problems of feature subset selection in most of applications. In the present study, we attempt to construct an informative novel tree kernel to address these problems. The constructed tree kernel can effectively discover the similarities of samples and handle nonlinear classification problems. Simultaneously, informative features can be evaluated by variable importance ranking in the process of building kernel by a large number of decision trees. Thus, under the framework of kernel methods, a novel tree kernel support vector machine (TKSVM) has been proposed to model the structure–activity relationship between bioactivities and molecular structures. Three datasets related to different categorical bioactivities of compounds are used to test the performance of TKSVM. The results show that the present method is a promising one compared to the SVM models with other commonly used kernels. [Copyright &y& Elsevier]

Abstract: Gas chromatography fingerprint of Scutellaria barbata D. Don (SB) from different origins was studied by gas chromatography–mass spectrometry (GC–MS) and related Chemometrics methods. The constituents of essential oil of Scutellaria barbata D. Don (SB) and its two adulterants Oldenlandia diffusa (OD) and Lobelia chinensis Lour (LCL) were analyzed and compared, 50, 36 and 38 components were identified from SB, OD and LCL, respectively, there were 16 and 18 common components between SB and OD, SB and LCL. Nine different samples collected from different producing areas of SB were studied. The relative standard deviations (RSDs) of retention time and peak area of each component were less than 0.2% and 6%, respectively. The number of common peaks was up to 52 and the un-common peak area was less than 10%. The similarity and difference among SB and its adulterants were also evaluated by correlation coefficient similarity analysis and principal component analysis, the result showed that developed fingerprint characterize the SB from different producing areas and it is useful and feasible for the discrimination of its adulterants. [Copyright &y& Elsevier]

Abstract: A combined approach of subwindow factor analysis and orthogonal projection resolution was used to analyze the volatile components of cut tobacco samples from different sources. After extracted with simultaneous distillation and extraction method, the volatile components in cut tobacco from five different locations were detected by GC–MS. Then, the qualitative and quantitative analysis of the volatile components of cut tobacco from Changde area was completed with the help of subwindow factor analysis resolving two-dimensional original data into pure mass spectra and chromatograms. One hundred and two volatile components among 138 separated peaks were identified and quantified, accounting for about 88.90% of the total content. Finally, orthogonal projection method was used to extract the common peaks from different locations. Among the identified components, there were 74 components coexisting in five studied samples although the relative content of each component showed difference to some extent. The results showed a fair consistency in their GC–MS fingerprints. It was the first time to apply orthogonal projection method to compare different cut tobacco samples, and it reduced the burden of qualitative analysis as well as the subjectivity. The obtained results proved the combined approach powerful for the analysis of complex cut tobacco samples. The developed method can be used to compare the sameness and differences of cut tobacco from different sources and for quality control of cigarette production and materials. [Copyright &y& Elsevier]