A new issue of this journal has just
been published. To see abstracts of the papers it contains (with links through
to the full papers) click here:
One-class partial least squares (OCPLS) classifier
30 May 2013,
09:25:22
Publication date: 15 July
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Lu Xu , Si-Min Yan , Chen-Bo Cai , Xiao-Ping Yu
One-class partial least squares (OCPLS) classifier is investigated as a tool for multivariate statistical quality control (MSQC). According to the OCPLS score distance (SD) and absolute centered residual (ACR) of predicted response, an object can be classified into one of the four groups: regular points (with a small SD and a small ACR), class outliers (with a small SD and a large ACR), good leverage points (with a large SD and a small ACR) and bad leverage points (with a large SD and a large ACR). The correlation between OCPLS distance measures and some existing methods, including D-statistic, Q-statistic and correlation coefficient (Pearson's r), is briefly discussed. OCPLS is applied to non-targeted detection of adulterations in whole milk powder using near-infrared (NIR) spectroscopy. The results demonstrate OCPLS can provide an effective tool for MSQC by including both SD and ACR of predicted response.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Lu Xu , Si-Min Yan , Chen-Bo Cai , Xiao-Ping Yu
One-class partial least squares (OCPLS) classifier is investigated as a tool for multivariate statistical quality control (MSQC). According to the OCPLS score distance (SD) and absolute centered residual (ACR) of predicted response, an object can be classified into one of the four groups: regular points (with a small SD and a small ACR), class outliers (with a small SD and a large ACR), good leverage points (with a large SD and a small ACR) and bad leverage points (with a large SD and a large ACR). The correlation between OCPLS distance measures and some existing methods, including D-statistic, Q-statistic and correlation coefficient (Pearson's r), is briefly discussed. OCPLS is applied to non-targeted detection of adulterations in whole milk powder using near-infrared (NIR) spectroscopy. The results demonstrate OCPLS can provide an effective tool for MSQC by including both SD and ACR of predicted response.
Quantitative Raman spectrometry: The accurate determination of analytes in solution phase of turbid media
30 May 2013,
09:25:22
Publication date: 15 July
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Jing Yang , Zeng-Ping Chen , Juan Zhang , Jing-Wen Jin , Yao Chen
The presence of scatterers in turbid media could severely distort the Raman measurements and thereby prevent accurate determination of analytes in turbid media. To address this issue, in this contribution, an advanced model, multiplicative effects model (MEM), has been derived to explicitly model the effects of scatterers on Raman measurements. Preliminary experimental results for a proof of concept system with varying turbidity levels demonstrated that MEM could effectively account for the detrimental multiplicative effects of scatterers and ultimately achieved accurate quantitative analysis of analyte of interest in turbid media with a relative prediction error of about 4.3%. The enhanced levels of accuracy obtained with MEM open up an avenue for prospective prediction studies in turbid media such as biological tissues by Raman spectrometry.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Jing Yang , Zeng-Ping Chen , Juan Zhang , Jing-Wen Jin , Yao Chen
The presence of scatterers in turbid media could severely distort the Raman measurements and thereby prevent accurate determination of analytes in turbid media. To address this issue, in this contribution, an advanced model, multiplicative effects model (MEM), has been derived to explicitly model the effects of scatterers on Raman measurements. Preliminary experimental results for a proof of concept system with varying turbidity levels demonstrated that MEM could effectively account for the detrimental multiplicative effects of scatterers and ultimately achieved accurate quantitative analysis of analyte of interest in turbid media with a relative prediction error of about 4.3%. The enhanced levels of accuracy obtained with MEM open up an avenue for prospective prediction studies in turbid media such as biological tissues by Raman spectrometry.
Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching–learning-based optimization
30 May 2013,
09:25:22
Publication date: 15 July
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Guoqiang Li , Peifeng Niu , Weiping Zhang , Yongchao Liu
The teaching–learning-based optimization (TLBO) is a new efficient optimization algorithm. To improve the solution quality and to quicken the convergence speed and running time of TLBO, this paper proposes an ameliorated TLBO called A-TLBO and test it by classical numerical function optimizations. Compared with other several optimization methods, A-TLBO shows better search performance. In addition, the A-TLBO is adopted to adjust the hyper-parameters of least squares support vector machine (LS-SVM) in order to build NOx emissions model of a 330MW coal-fired boiler and obtain a well-generalized model. Experimental results show that the tuned LS-SVM model by A-TLBO has well regression precision and generalization ability.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Guoqiang Li , Peifeng Niu , Weiping Zhang , Yongchao Liu
The teaching–learning-based optimization (TLBO) is a new efficient optimization algorithm. To improve the solution quality and to quicken the convergence speed and running time of TLBO, this paper proposes an ameliorated TLBO called A-TLBO and test it by classical numerical function optimizations. Compared with other several optimization methods, A-TLBO shows better search performance. In addition, the A-TLBO is adopted to adjust the hyper-parameters of least squares support vector machine (LS-SVM) in order to build NOx emissions model of a 330MW coal-fired boiler and obtain a well-generalized model. Experimental results show that the tuned LS-SVM model by A-TLBO has well regression precision and generalization ability.
Rough set based wavelength selection in near-infrared spectral analysis
30 May 2013,
09:25:22
Publication date: 15 July
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Ying Dong , Bingren Xiang , Ying Geng , Wenbo Yuan
Rough set based procedure was proposed as a new methodology to select component-specific wavelengths for near-infrared (NIR) spectral analysis. Information gain (IG) was employed to regulate the size of the discernibility matrix and decrease the memory requirements of rough set based reduction. This procedure involved submitting the resulting subsets of wavelengths to the analytical models in question. The utility of this method was illustrated by an analysis of classification models for phenylalanine (Phe) in plasma. The wavelength selection algorithm was compared with correlation based feature selection (CRFS) method and consistency based feature selection (CSFS) approach. Model fit was assessed using 10-fold cross-validation (10-fold CV) and leave-one out (LOO) approach. The predictability of the model was evaluated by an external prediction set. Furthermore, another two NIR data sets, obtained from the published literatures, were used to develop the quantitative models and validate the rough set based wavelength selection method. This study demonstrates conclusively that reducts of rough set could preserve the spectra–structure relationship and provide reliable model variables for NIR analysis. The results also indicate that rough set algorithm may hold promise for application as an additional feasible technique to NIR band assignment. As a fast, simple and noninvasive measurement, it is hopeful to find a clinical use in the diagnosis of unusual Phe elevation with further research.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Ying Dong , Bingren Xiang , Ying Geng , Wenbo Yuan
Rough set based procedure was proposed as a new methodology to select component-specific wavelengths for near-infrared (NIR) spectral analysis. Information gain (IG) was employed to regulate the size of the discernibility matrix and decrease the memory requirements of rough set based reduction. This procedure involved submitting the resulting subsets of wavelengths to the analytical models in question. The utility of this method was illustrated by an analysis of classification models for phenylalanine (Phe) in plasma. The wavelength selection algorithm was compared with correlation based feature selection (CRFS) method and consistency based feature selection (CSFS) approach. Model fit was assessed using 10-fold cross-validation (10-fold CV) and leave-one out (LOO) approach. The predictability of the model was evaluated by an external prediction set. Furthermore, another two NIR data sets, obtained from the published literatures, were used to develop the quantitative models and validate the rough set based wavelength selection method. This study demonstrates conclusively that reducts of rough set could preserve the spectra–structure relationship and provide reliable model variables for NIR analysis. The results also indicate that rough set algorithm may hold promise for application as an additional feasible technique to NIR band assignment. As a fast, simple and noninvasive measurement, it is hopeful to find a clinical use in the diagnosis of unusual Phe elevation with further research.
A chemometric approach to prediction of transmembrane pressure in membrane bioreactors
30 May 2013,
09:25:2
Publication date: 15 July
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Hiromasa Kaneko , Kimito Funatsu
Membrane bioreactors (MBRs) have been widely used to purify wastewater for reuse. However, MBRs are subject to fouling, which is the phenomenon whereby foulants absorb or deposit on the membrane. To enable chemical cleaning to be performed at an appropriate time, membrane fouling must to be predicted in the long term. There has been research on correlations among fouling phenomena, water quality variables, and operating conditions. Therefore, in this paper, we aimed to construct a chemometric or statistical model between the increase in the transmembrane pressure (TMP) and MBR parameters such as water quality variables and operating conditions and use this model to predict TMP. We analyzed three data sets measured in real industrial MBR plants and then confirmed that the constructed model could predict TMP over time with high accuracy. By applying the proposed method to process control, MBR plants will be operated effectively and stably.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Hiromasa Kaneko , Kimito Funatsu
Membrane bioreactors (MBRs) have been widely used to purify wastewater for reuse. However, MBRs are subject to fouling, which is the phenomenon whereby foulants absorb or deposit on the membrane. To enable chemical cleaning to be performed at an appropriate time, membrane fouling must to be predicted in the long term. There has been research on correlations among fouling phenomena, water quality variables, and operating conditions. Therefore, in this paper, we aimed to construct a chemometric or statistical model between the increase in the transmembrane pressure (TMP) and MBR parameters such as water quality variables and operating conditions and use this model to predict TMP. We analyzed three data sets measured in real industrial MBR plants and then confirmed that the constructed model could predict TMP over time with high accuracy. By applying the proposed method to process control, MBR plants will be operated effectively and stably.
A SEVA soft sensor method based on self-calibration model and uncertainty description algorithm
30 May 2013,
09:25:22
Publication date: 15 July
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Liu Yiqi , Huang Daoping , Li Zhifu
Soft sensors are widely used to estimate process variables that are difficult to measure online. However, due to poor quality of input data and deterioration of prediction model as time passes, soft sensors make poor performance. We have been constructing a principal component analysis (PCA) model before performing a prediction. Furthermore, the just-in-time (JIT) learning model has been improved and served as prediction model for self validating (SEVA) soft sensors. The proposed soft sensor not only carries out internal quality assessment but also generates multiple types of output data, including the prediction values (PV), input sensor status (ISS), validated measurement (VM), output sensor status (OSS) and the uncertainty values (UV) which represent the credibility of soft sensors' output. The effectiveness of the proposed SEVA soft sensors is demonstrated through a case study of a wastewater treatment process.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Liu Yiqi , Huang Daoping , Li Zhifu
Soft sensors are widely used to estimate process variables that are difficult to measure online. However, due to poor quality of input data and deterioration of prediction model as time passes, soft sensors make poor performance. We have been constructing a principal component analysis (PCA) model before performing a prediction. Furthermore, the just-in-time (JIT) learning model has been improved and served as prediction model for self validating (SEVA) soft sensors. The proposed soft sensor not only carries out internal quality assessment but also generates multiple types of output data, including the prediction values (PV), input sensor status (ISS), validated measurement (VM), output sensor status (OSS) and the uncertainty values (UV) which represent the credibility of soft sensors' output. The effectiveness of the proposed SEVA soft sensors is demonstrated through a case study of a wastewater treatment process.
A toolbox to explore NMR metabolomic data sets using the R environment
30 May 2013,
09:25:22
Publication date: 15 July
2013
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Stéphane Balayssac , Sébastien Déjean , Julie Lalande , Véronique Gilard , Myriam Malet-Martino
We describe herein the implementation of graphical and statistical tools developed in the R free software environment to explore metabolomic data sets. This toolbox, available upon request from the authors for the latest releases, includes univariate, bivariate and multivariate existing approaches accompanied with various graphical displays and interactive facilities. Concretely, very basic knowledge in R is required: from Excel data files as input to graphical and numerical outputs the user is led through a set of questions he only has to answer. We illustrate the potential of the toolbox on a data set coming from a 1H NMR metabolomic study of cerebellums from a murine model of Alzheimer's disease. We show the complementarity of various graphical techniques in order to provide information easier to interpret. In particular, a simple correlation study can be highly meaningful, and competitive with a more sophisticated multivariate analysis, when using ad hoc graphical representations depending on the level of interest: global, multiple or single metabolite focus.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 126
Author(s): Stéphane Balayssac , Sébastien Déjean , Julie Lalande , Véronique Gilard , Myriam Malet-Martino
We describe herein the implementation of graphical and statistical tools developed in the R free software environment to explore metabolomic data sets. This toolbox, available upon request from the authors for the latest releases, includes univariate, bivariate and multivariate existing approaches accompanied with various graphical displays and interactive facilities. Concretely, very basic knowledge in R is required: from Excel data files as input to graphical and numerical outputs the user is led through a set of questions he only has to answer. We illustrate the potential of the toolbox on a data set coming from a 1H NMR metabolomic study of cerebellums from a murine model of Alzheimer's disease. We show the complementarity of various graphical techniques in order to provide information easier to interpret. In particular, a simple correlation study can be highly meaningful, and competitive with a more sophisticated multivariate analysis, when using ad hoc graphical representations depending on the level of interest: global, multiple or single metabolite focus.
No comments:
Post a Comment