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Selected
papers from the latest issue:
Modeling and correction of Raman and Rayleigh scatter in fluorescence landscapes
05 November 2013,
09:21:02
Publication date: 15 January
2014
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Paul H.C. Eilers , Pieter M. Kroonenberg
Rayleigh and Raman scatter in fluorescence (excitation–emission) landscapes are a nuisance in two-way and three-way data modeling. We provide a method to clean individual emission spectra. The scatter can be represented accurately by Gaussian peaks, characterized by location, width and height. The analytic signal of interest effectively acts as a background to the scatter peaks. Modeling it locally as a smooth curve, using penalized least squares, allows accurate estimation of the parameters of scatter peaks. Once the peaks are modeled, they can be subtracted from the spectrum, almost completely removing the artifacts. Apart from local smoothness, no assumptions are made about the fluorescence spectra.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Paul H.C. Eilers , Pieter M. Kroonenberg
Rayleigh and Raman scatter in fluorescence (excitation–emission) landscapes are a nuisance in two-way and three-way data modeling. We provide a method to clean individual emission spectra. The scatter can be represented accurately by Gaussian peaks, characterized by location, width and height. The analytic signal of interest effectively acts as a background to the scatter peaks. Modeling it locally as a smooth curve, using penalized least squares, allows accurate estimation of the parameters of scatter peaks. Once the peaks are modeled, they can be subtracted from the spectrum, almost completely removing the artifacts. Apart from local smoothness, no assumptions are made about the fluorescence spectra.
Dimensionality choice in principal components analysis via cross-validatory methods
05 November 2013,
09:21:02
Publication date: 15 January
2014
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Peyman Eshghi
This paper considers cross-validation based approaches to automatically determine the appropriate number of dimensions to retain in a Principal Components Analysis (PCA). Three approaches based on a mixture of leaving groups of observations and variables out are described. They are compared through simulation across a range of datasets of differing sizes and differing levels of missingness using the NIPALS algorithm to carry out the PCA. Also included in the paper is an explicit description of how the NIPALS algorithm is implemented to deal with missing data. Finally we provide suggestions as to which approach offers a better compromise between reliability in choosing the optimal number of components, and the computational burden.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Peyman Eshghi
This paper considers cross-validation based approaches to automatically determine the appropriate number of dimensions to retain in a Principal Components Analysis (PCA). Three approaches based on a mixture of leaving groups of observations and variables out are described. They are compared through simulation across a range of datasets of differing sizes and differing levels of missingness using the NIPALS algorithm to carry out the PCA. Also included in the paper is an explicit description of how the NIPALS algorithm is implemented to deal with missing data. Finally we provide suggestions as to which approach offers a better compromise between reliability in choosing the optimal number of components, and the computational burden.
Support vector regression in sum space for multivariate calibration
05 November 2013,
09:21:02
Publication date: 15 January
2014
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Jiangtao Peng , Luoqing Li
In this paper, a support vector regression algorithm in the sum of reproducing kernel Hilbert spaces (SVRSS) is proposed for multivariate calibration. In SVRSS, the target regression function is represented as the sum of several single kernel decision functions, where each single kernel function with specific scale can approximate certain component of the target function. For sum spaces with two Gaussian kernels, the proposed method is compared, in terms of RMSEP, to traditional chemometric PLS calibration methods and recent promising SVR, GPR and ELM methods on a simulated data set and four real spectroscopic data sets. Experimental results demonstrate that SVR methods outperform PLS methods for spectroscopy regression problems. Moreover, SVRSS method with multi-scale kernels improves the single kernel SVR method and shows superiority over GPR and ELM methods.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Jiangtao Peng , Luoqing Li
In this paper, a support vector regression algorithm in the sum of reproducing kernel Hilbert spaces (SVRSS) is proposed for multivariate calibration. In SVRSS, the target regression function is represented as the sum of several single kernel decision functions, where each single kernel function with specific scale can approximate certain component of the target function. For sum spaces with two Gaussian kernels, the proposed method is compared, in terms of RMSEP, to traditional chemometric PLS calibration methods and recent promising SVR, GPR and ELM methods on a simulated data set and four real spectroscopic data sets. Experimental results demonstrate that SVR methods outperform PLS methods for spectroscopy regression problems. Moreover, SVRSS method with multi-scale kernels improves the single kernel SVR method and shows superiority over GPR and ELM methods.
Statistical process monitoring based on a multi-manifold projection algorithm
05 November 2013,
09:21:02
Publication date: 15 January
2014
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Chudong Tong , Xuefeng Yan
Considering that the global and local structures of process data would probably be changed in some abnormal states, a multi-manifold projection (MMP) algorithm for process monitoring and fault diagnosis is proposed under the graph embedded learning framework. To exploit the underlying geometrical structure that contains both global and local information of sampled data, the global graph and local graph are designed to characterize the global and local structures, respectively. A unified optimization framework, i.e. global graph maximum and local graph minimum, is then constructed to extract meaningful low-dimensional representations for high-dimensional process data. In the proposed MMP, the neighborhood embedding is used in both global and local graphs and the extracted features are faithful representations of the original data. The feasibility and validity of the MMP-based process monitoring scheme are investigated through two case studies: a simple simulation process and the Tennessee Eastman process. The experimental results demonstrate that the whole performance of MMP is better than those of some traditional preserving global or local or global and local feature methods.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Chudong Tong , Xuefeng Yan
Considering that the global and local structures of process data would probably be changed in some abnormal states, a multi-manifold projection (MMP) algorithm for process monitoring and fault diagnosis is proposed under the graph embedded learning framework. To exploit the underlying geometrical structure that contains both global and local information of sampled data, the global graph and local graph are designed to characterize the global and local structures, respectively. A unified optimization framework, i.e. global graph maximum and local graph minimum, is then constructed to extract meaningful low-dimensional representations for high-dimensional process data. In the proposed MMP, the neighborhood embedding is used in both global and local graphs and the extracted features are faithful representations of the original data. The feasibility and validity of the MMP-based process monitoring scheme are investigated through two case studies: a simple simulation process and the Tennessee Eastman process. The experimental results demonstrate that the whole performance of MMP is better than those of some traditional preserving global or local or global and local feature methods.
Two-level PLS model for quality prediction of multiphase batch processes
05 November 2013,
09:21:02
Publication date: 15 January
2014
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Zhiqiang Ge , Zhihuan Song , Luping Zhao , Furong Gao
Statistical quality prediction methods for multiphase batch processes have gained much attention in recent years. While most methods are focused on the data information inside each phase, the relationships among different phases have rarely been explored and used for quality prediction, although it may have significant impacts on prediction of the final quality. In this paper, a two-level partial least squares (PLS) model is proposed, in which the relationships among different single phases are modeled and incorporated for quality prediction. In the first level of this method, a representative intraphase-PLS model is built for each single phase, while in the second level, a series of interphase-PLS models are constructed to capture the relationships among different phases. With the incorporation of the additional interphase information, the multiphase quality prediction performance can be improved, which is evaluated through an industrial case study.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Zhiqiang Ge , Zhihuan Song , Luping Zhao , Furong Gao
Statistical quality prediction methods for multiphase batch processes have gained much attention in recent years. While most methods are focused on the data information inside each phase, the relationships among different phases have rarely been explored and used for quality prediction, although it may have significant impacts on prediction of the final quality. In this paper, a two-level partial least squares (PLS) model is proposed, in which the relationships among different single phases are modeled and incorporated for quality prediction. In the first level of this method, a representative intraphase-PLS model is built for each single phase, while in the second level, a series of interphase-PLS models are constructed to capture the relationships among different phases. With the incorporation of the additional interphase information, the multiphase quality prediction performance can be improved, which is evaluated through an industrial case study.
Radial basis function network-based transformation for nonlinear partial least-squares as optimized by particle swarm optimization: Application to QSAR studies
05 November 2013,
09:21:02
Publication date: 15 January
2014
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Jing-Jing Xing , Rui-Min Luo , Hai-Li Guo , Ya-Qiong Li , Hai-Yan Fu , Tian-Ming Yang , Yan-Ping Zhou
This study presented a new version of the nonlinear partial least-squares method on the optimized radial basis function network transformation by particle swarm optimization (PSORBFPLS). This algorithm firstly transformed the training inputs into the hidden outputs using the nonlinear transformation carried by a radial basis function network (RBFN), and then employed linear partial least-squares (PLS) to relate the outputs of the hidden layer to the bioactivities. The widths and centers involved in RBF transformation were optimized by particle swarm optimization (PSO) with the minimized model error via PLS modeling as the criterion. The number of latent variables associated with PLS modeling was automatically identified by F-statistic. Two QSAR data sets were used to evaluate the performance of the newly proposed PSORBFPLS. Results of these two data sets demonstrated that PSORBFPLS offers substantially enhanced capacities in modeling nonlinearity while circumvents overfitting frequently encountered in nonlinear modeling.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Jing-Jing Xing , Rui-Min Luo , Hai-Li Guo , Ya-Qiong Li , Hai-Yan Fu , Tian-Ming Yang , Yan-Ping Zhou
This study presented a new version of the nonlinear partial least-squares method on the optimized radial basis function network transformation by particle swarm optimization (PSORBFPLS). This algorithm firstly transformed the training inputs into the hidden outputs using the nonlinear transformation carried by a radial basis function network (RBFN), and then employed linear partial least-squares (PLS) to relate the outputs of the hidden layer to the bioactivities. The widths and centers involved in RBF transformation were optimized by particle swarm optimization (PSO) with the minimized model error via PLS modeling as the criterion. The number of latent variables associated with PLS modeling was automatically identified by F-statistic. Two QSAR data sets were used to evaluate the performance of the newly proposed PSORBFPLS. Results of these two data sets demonstrated that PSORBFPLS offers substantially enhanced capacities in modeling nonlinearity while circumvents overfitting frequently encountered in nonlinear modeling.
A consensus PLS method based on diverse wavelength variables models for analysis of near-infrared spectra
05 November 2013,
09:21:02
Publication date: 15 January
2014
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Yankun Li , Jing Jing
A new method named as diverse variables-consensus partial least squares (DV-CPLS) is proposed based on consensus (ensemble) strategy combined with uninformative variable elimination (UVE) technique. In the approach, UVE-PLS is used to construct member models with different numbers of variables (wavelengths) instead of altering training subset in conventional consensus method, and then prediction results of multiple member models are combined by a new weighted averaging way to give ensemble results. DV-CPLS is applied for building quantitative model between diesel near-infrared (NIR) spectra and cetane number (CN), and the results show fine prediction capability in terms of accuracy and robustness. When DV-CPLS was further combined with wavelet transform (WT) method, a more parsimonious model was obtained. The proposed method improves the performance of conventional PLS linear modeling in determination of diesel CN by NIR spectra. So it is hoped that it will help further investigations of consensus modeling and variable selection technique, and as well as applications in the sphere of NIR and even other spectral analysis of sophisticated systems.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Yankun Li , Jing Jing
A new method named as diverse variables-consensus partial least squares (DV-CPLS) is proposed based on consensus (ensemble) strategy combined with uninformative variable elimination (UVE) technique. In the approach, UVE-PLS is used to construct member models with different numbers of variables (wavelengths) instead of altering training subset in conventional consensus method, and then prediction results of multiple member models are combined by a new weighted averaging way to give ensemble results. DV-CPLS is applied for building quantitative model between diesel near-infrared (NIR) spectra and cetane number (CN), and the results show fine prediction capability in terms of accuracy and robustness. When DV-CPLS was further combined with wavelet transform (WT) method, a more parsimonious model was obtained. The proposed method improves the performance of conventional PLS linear modeling in determination of diesel CN by NIR spectra. So it is hoped that it will help further investigations of consensus modeling and variable selection technique, and as well as applications in the sphere of NIR and even other spectral analysis of sophisticated systems.
Metabolomics research on time-selected combination of Liuwei Dihuang and Jinkui Shenqi pills in treating kidney deficiency and aging by chemometric methods
05 November 2013,
09:21:02
Publication date: 15 January
2014
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Liangxiao Zhang , Xiaofei Han , Zhan Li , Renhui Liu , Wenjuan Xu , Chunlan Tang , Xiujuan Wang , Hongbin Xiao
Jinkui Shenqi pill (JKSQ) and Liuwei Dihuang (LWDH) pills are ancient traditional Chinese medicines (TCMs), which are usually used for the treatment of kidney deficiency for thousands of years in China. Time-selected combination of LWDH and JKSQ pills in treating kidney deficiency and aging is one of the features of traditional Chinese medicines (TCMs). However, the molecular mechanisms of time-selected combination remain unclear. In this work, UHPLC–QTOF/MS based metabolomics research was conducted to evaluate the therapeutic effect of LWDH, JKSQ pills and their combinations on kidney deficiency in Sprague–Dawley rats induced by d-galactose and Dexamethasone. Based on peak areas of serum extracts, analysis of variance (ANOVA) and graphical index of separation (GIOS) were employed to select the significant variables for kidney deficiency and aging and principal component projection (PCP) was subsequently applied to evaluate the influence of drugs on endogenous metabolites. 10 endogenous metabolites from 22 important ions were identified via database search. The score plot of PCA shows that nourishing Yang–nourishing Yin group shows the strongest rehabilitation for metabolic disorder induced by kidney deficiency and aging, which is consistent with the classic theories of traditional Chinese medicine. Moreover, metabolic pathway function analysis indicates that kidney deficiency and aging might possess closed relationships with lipid metabolism and energy metabolism. In this work, the change trends of potential biomarkers after administration provide molecular evidence for combined administration of Jinkui Shenqi pill in the morning and Liuwei Dihuang pill at night for the patients with kidney deficiency. The method proposed in this study may provide inspiration for evaluation of the therapeutic effect of Chinese medicines by comparing the rehabilitation of potential biomarkers.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 130
Author(s): Liangxiao Zhang , Xiaofei Han , Zhan Li , Renhui Liu , Wenjuan Xu , Chunlan Tang , Xiujuan Wang , Hongbin Xiao
Jinkui Shenqi pill (JKSQ) and Liuwei Dihuang (LWDH) pills are ancient traditional Chinese medicines (TCMs), which are usually used for the treatment of kidney deficiency for thousands of years in China. Time-selected combination of LWDH and JKSQ pills in treating kidney deficiency and aging is one of the features of traditional Chinese medicines (TCMs). However, the molecular mechanisms of time-selected combination remain unclear. In this work, UHPLC–QTOF/MS based metabolomics research was conducted to evaluate the therapeutic effect of LWDH, JKSQ pills and their combinations on kidney deficiency in Sprague–Dawley rats induced by d-galactose and Dexamethasone. Based on peak areas of serum extracts, analysis of variance (ANOVA) and graphical index of separation (GIOS) were employed to select the significant variables for kidney deficiency and aging and principal component projection (PCP) was subsequently applied to evaluate the influence of drugs on endogenous metabolites. 10 endogenous metabolites from 22 important ions were identified via database search. The score plot of PCA shows that nourishing Yang–nourishing Yin group shows the strongest rehabilitation for metabolic disorder induced by kidney deficiency and aging, which is consistent with the classic theories of traditional Chinese medicine. Moreover, metabolic pathway function analysis indicates that kidney deficiency and aging might possess closed relationships with lipid metabolism and energy metabolism. In this work, the change trends of potential biomarkers after administration provide molecular evidence for combined administration of Jinkui Shenqi pill in the morning and Liuwei Dihuang pill at night for the patients with kidney deficiency. The method proposed in this study may provide inspiration for evaluation of the therapeutic effect of Chinese medicines by comparing the rehabilitation of potential biomarkers.
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