Yi-Zeng Liang

Monte Carlo cross validation

Abstract In order to choose correctly the dimension of calibration model in chemistry, a new simple and effective method named Monte Carlo cross validation (MCCV) is introduced in the present work. Unlike leave-one-out procedure commonly used in chemometrics for cross validation (CV), the Monte Carlo cross validation developed in this paper is an asymptotically consistent method in determining the number of components in calibration model. It can avoid an unnecessary large model and therefore decreases the risk of over-fitting for the calibration model. The results obtained from simulation study showed that MCCV has an obviously larger probability than leave-one-out CV in choosing the correct number of components that the model should contain. The results from real data sets demonstrated that MCCV could successfully choose the appropriate model, but leave-one-out CV could not.

Information theory applied to chromatographic fingerprint of herbal medicine for quality control.

At present, the construction of chromatographic fingerprints plays an important role in the quality control of complex herbal medicines. In this work, information theory was applied to obtain chromatographic fingerprints with good performance. Moreover, according to the characteristics of the chromatographic fingerprints obtained, some modifications of the calculation of the information content were conducted. In comparison with the information content from several chromatographic fingerprints obtained, reliable chromatographic fingerprints with a high separation degree and uniform concentration distribution of chemical components could be determined. The successful application of information theory with modification to simulated chromatographic fingerprints together with real herbal medicines such as Rhizoma chuanxiong and Ginkgo biloba from different sources demonstrated clearly that the proposed method to determine chromatographic fingerprints was reasonable and reliable and it was user-friendly. Chromatographic fingerprints determined with high separation degrees and uniform concentration distribution of chemical ingredients might also chemically represent characteristic components of herbal medicines for quality control.

propy: a tool to generate various modes of Chou’s PseAAC

SUMMARY Sequence-derived structural and physiochemical features have been frequently used for analysing and predicting structural, functional, expression and interaction profiles of proteins and peptides. To facilitate extensive studies of proteins and peptides, we developed a freely available, open source python package called protein in python (propy) for calculating the widely used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes five feature groups composed of 13 features, including amino acid composition, dipeptide composition, tripeptide composition, normalized Moreau-Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors, composition, transition and distribution of various structural and physicochemical properties and two types of pseudo amino acid composition (PseAAC) descriptors. These features could be generally regarded as different Chous PseAAC modes. In addition, it can also easily compute the previous descriptors based on user-defined properties, which are automatically available from the AAindex database. AVAILABILITY The python package, propy, is freely available via http://code.google.com/p/protpy/downloads/list, and it runs on Linux and MS-Windows. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Uniform design and its applications in chemistry and chemical engineering

Abstract Experimental designs are very important in chemometrics, since chemistry, until now, is still essentially a field of science strongly dependent on chemical experiments. So, for a long time, chemists have been trying to use the techniques of experimental design developed in statistics to improve experimental works. Three major methods of experimental design, such as factorial design including fractional factorial design and orthogonal design (OD), D-optimal design and uniform design (UD), and their applications in chemistry and chemical engineering are reviewed and compared. The features of uniform design are especially addressed. Uniformity of space filling is the most important and essential feature of the uniform design. Based on this feature, its cost-efficiency, robustness and flexibility make it very useful in the fields of chemistry and chemical engineering. Examples of successful applications of uniform design on improving technologies of various fields such as textile industry, pharmaceuticals, fermentation industry and others have been consistently reported in China since the 1980s. More recently, several various cases applying uniform design to chemical researches showed that the uniform design is indeed a very promising and powerful experimental design method.

Subwindow factor analysis

Abstract The method of subwindow factor analysis (SFA) is introduced as a solution to the problem of directly extracting component spectra from overlapping structures obtained from hyphenated chromatography without first resolving concentration profiles. This is of advantage when a complete resolution cannot be obtained or is of less interest in the analytical situation. The method is based upon comparisons of chromatographic regions (subwindows) which have only one eluting component in common. The paper presents the theory and an application to a structure with 4 overlapping components from a data set from a mixture of polyaromatic hydrocarbons recorded by high-performance liquid chromatography diode array detection (HPLC-DAD).

Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS-LDA

Metabolic profiling has increasingly been used as a probe in disease diagnosis and pharmacological analysis. Herein, plasma fatty acid metabolic profiling including non‐esterified fatty acid (NEFA) and esterified fatty acid (EFA) was investigated using gas chromatography/mass spectrometry (GC/MS) followed by multivariate statistical analysis. Partial least squares‐linear discrimination analysis (PLS‐LDA) model was established and validated to pattern discrimination between type 2 diabetic mellitus (DM‐2) patients and health controls, and to extract novel biomarker information. Furthermore, the PLS‐LDA model visually represented the alterations of NEFA metabolic profiles of diabetic patients with abdominal obesity in the treated process with rosiglitazone. The GC/MS‐PLS‐LDA analysis allowed comprehensive detection of plasma fatty acid, enabling fatty acid metabolic characterization of DM‐2 patients, which included biomarkers different from health controls and dynamic change of NEFA profiles of patients after treated with medicine. This method might be a complement or an alternative to pathogenesis and pharmacodynamics research.

Determination of volatile components in peptic powder by gas chromatography-mass spectrometry and chemometric resolution.

Gas chromatography-mass spectrometry (GC-MS) coupled with chemometric resolution upon two-dimensional data was proposed as a method for the analysis of volatile components in a traditional Chinese medicinal preparation peptic powder which contains Rhizoma Atractylodis, Pericarpium Citri Reticulatae, Cortex Magnoliae Officinalis and Radix Glycyrrhizae. Ninety-three components were separated and 65 of them were qualitatively and quantitatively analyzed which represented about 90.28% of the total content. With the help of chemometric resolution, the data were resolved into a pure chromatogram and a mass spectrum of each chemical component. The accuracy of qualitative and quantitative results was greatly improved by using the two-dimensional comprehensive information of chromatograms and mass spectra. The example showed that chemometric resolution could greatly enhance separation ability. This makes it possible to analyze complicated practical systems like traditional Chinese medicinal preparations with the help of coupled instruments and chemometric resolution methods.

Traditional Chinese medicine and separation science

Traditional Chinese medicines (TCMs) are getting more and more popular nowadays in the whole world for improving health condition of human beings as well as preventing and healing diseases. TCM is a multi-component system with components mostly unknown, and only a few compounds are responsible for the pharmaceutical and/or toxic effects. The large numbers of other components in the TCM make the screening and analysis of the bioactive components extremely difficult. So, separation and analysis of the desired chemical components in TCM are very important subjects for modernization research of TCM. Thus, many novel separation techniques with significant advantages over conventional methods were introduced and applied to separation and analysis of the chemical constituents in TCM. This review presents just a brief outline of the applications of different separation methods for the isolation and analysis of TCM constituents.

Evolving factor analysis in the presence of heteroscedastic noise

Abstract Evolving factor analysis (EFA) is a promixing method for the analysis of multivariate data with an intrinsic order. When applying EFA for assessment of peak homogeneity in liquid chromatography, one has to be aware of instrumental and experimental difficulties. Heteroscedasticity is one of the most serious problems and leads to additional eigenvalues that may be misinterpreted as being due to an impurity. After appropriate data pretreatment, the fixed-size window EFA technique proved successful for peak purity control in liquid chromatography with photodiode-array detection. Less than 1% of a spectrally similar impurity could be detected for Rs values as low as 0.3.

Gas chromatography–mass spectrometry and chemometric resolution applied to the determination of essential oils in Cortex Cinnamomi

In this paper, a novel procedure for qualitative and quantitative analysis of the two-dimensional data obtained from GC-MS is investigated to determine chemical components of essential oils in Cortex Cinnamomi from four different producing areas. A new method named iterative optimization procedure (IOP) specially used to resolve embedded peaks is also developed. With the help of IOP and other chemometric techniques, such as heuristic evolving latent projections, evolving factor analysis, sub-window factor analysis and orthogonal projection resolution, and etc., the detection of the purity of chromatographic peaks can be first addressed, and then the overlapping peaks are resolved into the pure chromatogram and mass spectrum of each component. The similarity searches in the MS database are finally conducted to qualitatively determine the chemical components. The results obtained showed that the accuracy of qualitative and quantitative analysis could be greatly enhanced by chemometric resolution methods. The chemometric resolution techniques upon the two-dimensional data can be quite promising tools for the analysis of the complex samples like traditional Chinese medicine.