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Chinese Journal of Chemistry 2013年第4期31卷 545-550页

作者： Weiwei Yu wensheng cai Xueguang Shao State Key Laboratory of Medicinal Chemical Biology Research Center for Analytical Sciences College of Chemistry Nankai University Tianjin 300071 China

A method for fast determination of the component in complex samples by using gas chromatography-mass spectrometry （GC-MS） was developed and used for quantitative analysis of phenanthrene in soils. In the method, window independent component analysis （WICA） was used for resolving the mass spectrum and non-negative im- mune algorithm （NNIA） was employed for obtaining the chromatographic profile. Therefore, spectral and chroma- tographic information of a specific component can be obtained from the measured GC-MS data of overlapping and high background. Six soil samples collected from different places were analyzed. The tedious pretreatments in pre- paring the samples and the elution in the separation were simplified for speeding up the analysis. Due to the com- plexity of the matrix, standard addition method was adopted for the final quantification. The applicability of the method was validated with a spiked sample and the results of the six samples are reasonable.

关键词： phenanthrene gas chromatography-mass spectrometry standard addition method chemoinformatics immune algorithm

Predicting Chromatographic Retention Time of C10-Chlorinated Paraffins in Gas Chromatography-Mass Spectrometry Using Quantitative Structure Retention Relationship

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Chemical Research in Chinese Universities 2015年第2期31卷 192-197页

作者： XIA Zhenzhen cai wensheng SHAO Xueguang Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) State Key Laboratory of Medicinal Chemical Biology Research Center for Analytical Sciences College of Chemistry Nankai University Tianjin 300071 P. R. China

Chlorinated paraffins（CPs） are potential persistent organic pollutants（POPs）, which threat the safety of environment and organisms. However, the analysis of CPs is a difficult task due to their complex composition containing thousands of congeners. In the present work, quantitative structure retention relationship（QSRR） of CPs was studied. A total of 470 molecular descriptors were generated, for describing the structures of 28 CPs and 12 descriptors relevant to retention time of the CPs were selected by stepwise regression. Then, QSRR models between retention time on the one hand and the selected descriptors on the other hand were established by multiple linear regres- sion（MLR）, partial least squares（PLS） and least square support vector regression（LS-SVR）. The result shows that PLS model is better than MLR and LS-SVR, obtaining a squared correlation coefficient（r2） of 0.9996 and a root mean squared error（RMSE） of 0.015. The PLS model was then used to predict the retention time of 49 C10-CPs. Three of them were investigated by gas chromatography coupled with mass spectrometry（GC-MS）. A well-defined correlation was found between the measured retention time and the predicted value.

关键词： Chlorinated paraffin Multivariate calibration Retention time Quantitative structure retention relationship

A post-modification approach to independent compo-nent analysis for resolution of overlapping GC/MS signals: from independent components to chemical components

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Science China Chemistry 2007年第4期50卷 530-537页

作者： WANG Wei cai wensheng SHAO XueGuang Department of Chemistry University of Science and Technology of ChinaHefei 230026China Department of Chemistry Nankai UniversityTianjin 300071China

Independent component analysis (ICA) has demonstrated its power to extract mass spectra from over-lapping GC/MS signal. However, there is still a problem that mass spectra with negative peaks at some m/z will be obtained in the resolved results when there are overlapping peaks in the mass spectra of a mixture. Based on a detail theoretical analysis of the preconditions for ICA and the non-negative property of GC/MS signals, a post-modification based on chemical knowledge (PMBK) strategy is pro-posed to solve this problem. By both simulated and experimental GC/MS signals, it was proved that the PMBK strategy can improve the resolution effectively.

关键词： independent component analysis (ICA) post modification immune algorithm (IA) GC/MS

A variable differential consensus method for improving the quantitative near-infrared spectroscopic analysis

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Science China Chemistry 2012年第9期55卷 1946-1952页

作者： DU GuoRong cai wensheng SHAO XueGuang Research Center for Analytical Sciences College of ChemistryNankai UniversityTianjin 300071China

Consensus methods have presented promising tools for improving the reliability of quantitative models in near-infrared(NIR) spectroscopic analysis.A strategy for improving the performance of consensus methods in multivariate calibration of NIR spectra is *** the approach,a subset of non-collinear variables is generated using successive projections algorithm(SPA) for each variable in the reduced spectra by uninformative variables elimination(UVE).Then sub-models are built using the variable subsets and the calibration subsets determined by Monte Carlo(MC) re-sampling,and the sub-model that produces minimal error in cross validation is selected as a member *** repetition of the MC re-sampling,a series of member models are built and a consensus model is achieved by averaging all the member *** member models are built with the best variable subset and the randomly selected calibration subset,both the quality and the diversity of the member models are insured for the consensus *** NIR spectral datasets of tobacco lamina are used to investigate the proposed *** superiority of the method in both accuracy and reliability is demonstrated.

关键词： near infrared spectroscopy multivariate calibration consensus model variable selection uninformative variable elim-ination successive projections algorithm

Cancer classification based on microarray gene expression data using a principal component accumulation method

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Science China Chemistry 2011年第5期54卷 802-811页

作者： LIU JingJing cai wensheng SHAO XueGuang Research Center for Analytical Sciences College of ChemistryNankai UniversityTianjin 300071China

The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation (PCAcc) method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.

关键词： cancer classification principal component analysis principal component accumulation gene expression data

Deciphering the Mechanism Involved in the Switch On/Off of Molecular Pistons

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Chinese Journal of Chemistry 2015年第10期33卷 1199-1205页

作者： Peng Liu Xueguang Shao wensheng cai State Key Laboratory of Medicinal Chemical Biology Nankai University Tianjin 300071 China Collaborative Innovation Center of Chemical Science andEngineering (Tianjin) Tianjin 300072 China Research Center for Analytieal Sciences College of Chemistry Nankai University Tianjin 300071 China

Manufacturing machines converting energy to mechanical work at the molecular level is a vital pathway to ex- plore the microscopic world. A kind of operable molecular engines, composed offl-cyclodextrin （fl-CD）, aryl, al- kene and amide moiety was investigated using molecular dynamics simulations combined with free-energy calcula- tions. To understand how the integrated alkene double bond controls the work performed on the engines, two alkene isomers of the prototype were considered as two molecular engines. The free-energy profiles delineating the binding process of the amide （Z）- and （E）-isomers for each alkene isomer with 1-adamantanol indicate that for the alkene （E）-isomer, the apparent work performed on the amide bond is 1.6 kcal/mol, while the alkene （Z）-isomer is incapa- ble to perform work. Direct switch on/off of engines caused by the isomerization of the alkene bond was, therefore, witnessed, in line with experimental measurements. Decomposition of the free-energy profile into different compo- nents and structural analyses suggest that the isomerization of the alkene bond controls the position of the aryl unit relative to the cavity of the CD, resulting in the difference among the free-energy profiles and the stark contrast of the work performed on engines.

关键词： molecular pistons cyelodextrins cis-trans isomerization molecular dynamics simulations

Outlier detection in near-infrared spectroscopic analysis by using Monte Carlo cross-validation

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Science China Chemistry 2008年第8期51卷 751-759页

作者： LIU ZhiChao cai wensheng SHAO XueGuang Research Center for Analytical Sciences College of ChemistryNankai UniversityTianjin 300071China

An outlier detection method is proposed for near-infrared spectral analysis. The underlying philosophy of the method is that,in random test(Monte Carlo) cross-validation,the probability of outliers presenting in good models with smaller prediction residual error sum of squares(PRESS) or in bad models with larger PRESS should be obviously different from normal samples. The method builds a large number of PLS models by using random test cross-validation at first,then the models are sorted by the PRESS,and at last the outliers are recognized according to the accumulative probability of each sample in the sorted models. For validation of the proposed method,four data sets,including three published data sets and a large data set of tobacco lamina,were investigated. The proposed method was proved to be highly efficient and veracious compared with the conventional leave-one-out(LOO) cross validation method.

关键词： near-infrared spectrum partial least squares(PLS) Monte Carlo cross validation Outlier detection

Chemometric methods for extracting information from temperature-dependent near-infrared spectra

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Science China Chemistry 2019年第5期62卷 583-591页

作者： Xiaoyu Cui Yan Sun wensheng cai Xueguang Shao Research Center for Analytical Sciences College of ChemistryNankai University Tianjin Key Laboratory of Biosensing and Molecular Recognition State Key Laboratory of Medicinal Chemical Biology Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Xinjiang Laboratory of Native Medicinal and Edible Plant Resources Chemistry College of Chemistry and Environmental ScienceKashgar University

Temperature-dependent near-infrared(NIR) spectroscopy is a new technique for measuring the NIR spectra of a sample at different temperatures. Taking the advantage of the temperature effect, the technique has shown its potential in both quantitative and qualitative analysis. The technique has been proved to be powerful in determination of the analytes in complex samples,particularly in studying the functions of water in aqueous systems due to the significant effect of temperature on the NIR spectra of water. Because of the complicated interactions in the samples and the overlapping of the broad peaks in NIR spectra, it is difficult to extract the temperature-dependent information from the spectra. Chemometric methods, therefore, have been developed for improving the spectral resolution and extracting the temperature-induced spectral information. In this review, recent advances in the studies of chemometric methods and the applications in resolution, quantitative and structural analysis of temperature-dependent NIR spectra were summarized.

关键词： near-infrared spectroscopy temperature effect chemometrics water aquaphotomics

A variable importance criterion for variable selection in near-infrared spectral analysis

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Science China Chemistry 2019年第2期62卷 271-279页

作者： Jin Zhang Xiaoyu Cui wensheng cai Xueguang Shao Research Center for Analytical Sciences College of Chemistry Nankai University Tianjin Key Laboratory of Biosensing and Molecular Recognition State Key Laboratory of Medicinal Chemical Biology Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Xinjiang Laboratory of Native Medicinal and Edible Plant Resources Chemistry College of Chemistry and Environmental ScienceKashgar University

Variable selection is a universal problem in building multivariate calibration models, such as quantitative structure-activity relationship(QSAR) and quantitative relationships between quantity or property and spectral data. Significant improvement in the prediction ability of the models can be achieved by reducing the bias induced by the uninformative variables. A new criterion,named as C, is proposed in this study to evaluate the importance of the variables in a model. The value of C is defined as the average contribution of a variable to the model, which is calculated by the statistics of the models built with different combinations of the variables. In the calculation, a large number of partial least squares(PLS) models are built using a subset of variables selected by randomly re-sampling. Then, a vector of the prediction errors, in terms of root mean squared error of cross validation(RMSECV), and a matrix composed of 1 and 0 indicating the selected and unselected variables can be obtained. If multiple linear regression(MLR) is employed to model the relationship between the RMSECVs and the matrix, the coefficients of the MLR model can be used as a criterion to evaluate the contribution of a variable to the RMSECV. To enhance the efficiency of the method, a multi-step shrinkage strategy was used. Comparison with Monte Carlo-uninformative variables elimination(MC-UVE), randomization test(RT) and competitive adaptive reweighted sampling(CARS) was conducted using three NIR benchmark datasets. The results show that the proposed criterion is effective for selecting the informative variables from the spectra to improve the prediction ability of models.

关键词： near-infrared spectroscopy variable selection multivariate calibration multi-step strategy

Determination of Cysteine Using Near-infrared Diffuse Reflectance Spectroscopy with Enrichment via Thiol-maleimide Click Reaction

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Chemical Research in Chinese Universities 2016年第6期32卷 912-916页

作者： WANG Cuicui cai wensheng SHAO Xueguang Research Center for Analytical Sciences College of Chemistry Nankai University Tianjin 300071 P. R. China Key Laboratory of Biosensing and Molecular Recognition Nankai University Tianjin 3000 71 P. R. China State Key Laboratory of Medicinal Chemical Biology Nankai University Tianjin 300071 P. R. China Collaborative Innovation Center of Chemical Science and Engineering Nankai University Tianjin 300071 R R. China

Enrichment technique has been proved to be an efficient way to make the near-infrared diffuse reflectancespectroscopy（NIRDRS） suitable for micro analysis. However, low selectivity presented by conventional enrichmentmethods makes the quantitative analysis easy to be affected by the coexisting components. In this study, a specificenrichment method with chemical bonding via thiol-maleimide click reaction was used to achieve the reduction of theinterferences. Taking cysteine as the analyzing target, maleimide-functionalized SiO2 nanoparticles were prepared forthe enrichment of cysteine. Then determination of cysteine in aqueous solution and human serum was studied usingthe partial least squares model built from the NIRDRS spectra of the adsorbate. The results show that the concentra-tion that can be quantitatively detected is as low as 2.0 μg/mL, and the correlation coefficient（R） between thereference and predicted concentration is 0.9871 for the validation samples. The recoveries are in the range of89.5%-113.8% for human serum samples in the concentration range of 0--16.2 μg/mL.

关键词： Near-infrared diffuse reflectance spectroscopy Click reaction Cysteine Multivariate calibration Humanserum