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Selected
papers from the latest issue:
Modeling and correction of Raman and Rayleigh scatter in fluorescence landscapes
14 October 2013,
09:02:00
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
14 October 2013,
09:02:00
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
14 October 2013,
09:02:00
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
14 October 2013,
09:02:00
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.
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