A chemometric method to identify selective ion for resolution of overlapping gas chromatography-mass spectrometry signal

A chemometric method to determine selective ion by using non-negative immune algorithm (NNIA) was proposed. In the method, the mutual projections of the chromatographic profiles at different m/z channel are calculated using NNIA. Suppose a GC-MS data with m retention time points and n mass channels, the projections of the GC-MS data onto a chromatographic profile at a mass channel will form a mass spectrum of 1 n vector. If the chromatographic profile at a selective mass channel is used, the extracted mass spectrum will be a correct one. Therefore, by comparing the extracted mass spectrum with a reference spectrum, the selective ion can be identified, and the corresponding chromatographic profile can be obtained at the same time. GC-MS data of 40-pesticide mixture was investigated by the method. The results show that both the mass spectral and the chromatographic information of the interested components can be extracted from the overlapping signals, except for the special cases of isomeric components with very similar mass spectra.     

环戊烯分子内价轨道1a′的电子动量谱学研究

报道了环戊烯（C₅H₈）分子1a′轨道的电子动量谱，并且给出了价 轨道的电离能谱信息 .实验在非共面对称几何条件下用能量多道型电子动量谱仪完成，入射电子的能量为1200 eV 加结合能.通过Hartree-Fock 和密度泛函方法计算得到了C₅H₈分子 1a′轨道的动量谱. 对1a′轨道的电子动量谱的实验值和理论计算进行了比较，得到了该轨道的极强度（pole s trength）的信息.     

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

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 proposed.In 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 model.With repetition of the MC re-sampling,a series of member models are built and a consensus model is achieved by averaging all the member models.Since 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 model.Two NIR spectral datasets of tobacco lamina are used to investigate the proposed method.The superiority of the method in both accuracy and reliability is demonstrated.     

A practical approach for near infrared spectral quantitative analysis of complex samples using partial least squares modeling

The number of latent variables (LVs) or the factor number is a key parameter in PLS modeling to obtain a correct prediction. Although lots of work have been done on this issue, it is still a difficult task to determine a suitable LV number in practical uses. A method named independent factor diagnostics (IFD) is proposed for investigation of the contribution of each LV to the predicted results on the basis of discussion about the determination of LV number in PLS modeling for near infrared (NIR) spectra of complex samples. The NIR spectra of three data sets of complex samples, including a public data set and two tobacco lamina ones, are investigated. It is shown that several high order LVs constitute main contributions to the predicted results, albeit the contribution of the low order LVs should not be neglected in the PLS models. Therefore, in practical uses of PLS for analysis of complex samples, it may be better to use a slightly large LV number for NIR spectral analysis of complex samples. Supported by the National Natural Science Foundation of China (Grant Nos. 20775036 & 20835002)     

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

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.     

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

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 subsp...     

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

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.