Biomedical subjects

Boyan Li

Publications and source records attributed to Boyan Li.

6 recordsLinked to original sources

PubMed2006

Noise perturbation in functional principal component analysis filtering for two-dimensional correlation spectroscopy: its theory and application to infrared spectra of a poly(3-hydroxybutyrate) thin film.

A method based on noise perturbation in functional principal component analysis (NPFPCA) is for the first time introduced to overcome the noise interference problem in two-dimensional correlation spectroscopy (2D-COS). By the systematic addition of synthetic noise to the dynamic multivariate spectral data, the functional principal component analysis (FPCA) described in this report is able to accurately determine which eigenvectors are representing significant signals instead of noise in the original data. This feature is especially useful for the data reconstruction and noise filtering. Reconstructed data resulted from the smooth eigenvectors can produce much more reliable 2D correlation spectra by removing the correlation artifacts from noise, which in turn enable more accurate interpretation of the spectral variations. The usefulness of this method is demonstrated with a theoretical framework and applications to the 2D correlation analyses of both simulated data and temperature-dependent reflection-absorption infrared spectra of a poly(3-hydroxybutyrate) (PHB) thin film.

Absorption↗

PubMed2006

Exploring time-dependent structural changes during the cold crystallization process of isotactic polystyrene by infrared spectroscopy and multivariate curve resolution.

The present study attempts an application of Fourier transform infrared (FT-IR) spectroscopy in conjunction with multivariate curve resolution (MCR) techniques to explore the structural evolution of isotactic polystyrene (iPS) during the cold crystallization process. The focus of the present study is placed on the performance of MCR techniques, e.g., orthogonal projection (OP), alternating least squares (ALS), and fixed-size moving window evolving factor analysis (FSMWEFA), and the interpretability of spectral changes in the investigated chemical process. As a result, valuable information and conclusions about the structural evolution of iPS during the crystallization process can be extracted: when the amorphous phase of iPS changes, the ordering of the phenyl rings takes place first, and then the polymer chains adjust their local conformations to form short 3(1) helix structures. Furthermore, according to intensity profiles of the spectral variations, the ordering of the phenyl rings proceeds more intensely than the formation of ordered local chains, and the structural evolution of iPS occurs even during the induction period. The spectral variations resulting from the conformational changes in the 3(1) helical structures depend on the sequence length of the helical chains: the longer the polymer chain is, the smaller the corresponding band variations are. It has been demonstrated that the combination of FTIR spectroscopy and chemometric MCR techniques is very promising for the analysis of the crystallization process of polymers. MCR is a powerful tool for analyzing and visualizing spectral data and integrating them with other information, making spectral intensity variations more amenable to interpretation in order to explore the molecular dynamics of polymers.

Algorithms↗

PubMed2006

Multi-objective genetic algorithm-based sample selection for partial least squares model building with applications to near-infrared spectroscopic data.

In this study, multi-objective genetic algorithms (GAs) are introduced to partial least squares (PLS) model building. This method aims to improve the performance and robustness of the PLS model by removing samples with systematic errors, including outliers, from the original data. Multi-objective GA optimizes the combination of these samples to be removed. Training and validation sets were used to reduce the undesirable effects of over-fitting on the training set by multi-objective GA. The reduction of the over-fitting leads to accurate and robust PLS models. To clearly visualize the factors of the systematic errors, an index defined with the original PLS model and a specific Pareto-optimal solution is also introduced. This method is applied to three kinds of near-infrared (NIR) spectra to build PLS models. The results demonstrate that multi-objective GA significantly improves the performance of the PLS models. They also show that the sample selection by multi-objective GA enhances the ability of the PLS models to detect samples with systematic errors.

Algorithms↗

PubMed2004

Identification of structures of nitrogen-containing compounds in crude oils in conjunction with chemometric resolution.

A universal method was established for the systematically structural identification of nitrogen-containing compounds in crude oils. Pre-fractionation of the non-hydrocarbons in a crude oil sample into 7 fractions was performed by di-adsorption column chromatography using neutral aluminum oxide and silica gel; subsequent high-resolution separation of individual components was achieved by using capillary column gas chromatography, and compound types were detected by mass spectrometer. The two-dimensional data from the compounds in the fractions were further resolved by a chemometric method to obtained the deconvoluted chromatogram and mass spectrum of every compound, and then, the nitrogen-containing compounds were identified in combination with the retention indices. This method could relieve the difficulty of classical analysis in identifying those species with very low contents or incompletely separation, particularly in the cases where the authentic standards were not available for addition into the unknown samples in order to reveal what indeed existed in them. The structures of 168 nitrogen-containing compounds in a crude oil sample were determined by this method with satisfactory results.

Aluminum Oxide↗

PubMed2004

Systemic analysis of structures and contents of nitrogen-containing compounds and other non-hydrocarbons in crude oils in conjunction with chemometric resolution technique.

A method is described for the systemic identification and quantitative analysis of nitrogen-containing compounds and other non-hydrocarbons in crude oils. The pre-fractionation of a crude oil sample into 7 fractions was performed by di-adsorption column chromatography using neutral aluminum oxide and silica gel. A subsequent high-resolution separation of individual components was achieved by using capillary column gas chromatography, and compound types were detected by a mass spectrometer. In conjunction with a chemometric method, the compounds in the fractions were further resolved or separated, which made it possible to identify some nitrogen-containing compounds and other non-hydrocarbons in crude oils. To a certain extent, this method could relieve the difficulty of classical analysis in identifying those species with very low contents or incompletely separation, particularly in the cases where authentic standards were not available for addition into the unknown samples in order to reveal what indeed existed in them. The structures and contents of 168 nitrogen-containing compounds in one crude sample and 60 non-nitrogen-containing compounds in one of non-hydrocarbon fractions of this oil sample were determined, and the addition-recovery examination of some standard compounds showed that the analytical veracity was satisfactory.

Journal Article↗

PubMedDate not supplied

Orthogonalization of block variables by subspace-projection for quantitative structure property relationship (QSPR) research.

A subspace-projection method is developed to construct orthogonal block variable, which is originally from some kinds of series of topological indices or quantum chemical parameters. With the help of canonical correlation analysis, the orthogonal block variables were used to establish the structure-retention index correlation model. The regression of only few new orthogonal variables obtained by canonical correlation analysis against retention index shows significant improvement both in fitting and prediction ability of the correlation model. Moreover, the quantitative intercorrelation between the different block variables of topological indices can also be evaluated with the help of the subspace-projection technique proposed in this work.

Journal Article↗