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Selected papers from the latest issue ...Joint multiple adaptive wavelet regression ensembles
30 June 2011, 21:35:04
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 30 June 2011
David, Donald , Danny, Coomans , Yvette, Everingham
Multiple adaptive discrete wavelet transforms were applied during a multiple regression of spectroscopic data for the purpose of investigating the hypothesis – does the use of different wavelets, at different points, within a spectrum, elucidate predictive capability. The model investigated was a constrained stacking regression ensemble with individual regression models chosen initially by a Bayes Metropolis search. The ensemble approach provided the ability to combine different regression models that used different types of wavelets. Models were applied to a publically available dataset, pertaining to biscuit dough, of near infrared spectra, that were measured by a FOSS 5000, and laboratory measurements...
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 30 June 2011
David, Donald , Danny, Coomans , Yvette, Everingham
Multiple adaptive discrete wavelet transforms were applied during a multiple regression of spectroscopic data for the purpose of investigating the hypothesis – does the use of different wavelets, at different points, within a spectrum, elucidate predictive capability. The model investigated was a constrained stacking regression ensemble with individual regression models chosen initially by a Bayes Metropolis search. The ensemble approach provided the ability to combine different regression models that used different types of wavelets. Models were applied to a publically available dataset, pertaining to biscuit dough, of near infrared spectra, that were measured by a FOSS 5000, and laboratory measurements...
Construction of space-filling designs using WSP algorithm for high dimensional spaces
26 June 2011, 21:20:54
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 26 June 2011
J., Santiago , M.Claeys-Bruno , M., Sergent
In the computer experiments setting, if the relationship between the response and the inputs is unknown, then the purpose is to use designs that spread the points at which the response is observed evenly throughout the region. These designs are called Space-Filling Designs (SFD) and the most known are Latin Hypercubes (random, orthogonal, optimized) and low discrepancy sequences. But, simulation codes becoming more and more complex, high dimensional optimal designs are needed to study a high number of parameters (more than 20 parameters) and the construction proves difficult. The aim of this study is to explore a construction method of...
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 26 June 2011
J., Santiago , M.Claeys-Bruno , M., Sergent
In the computer experiments setting, if the relationship between the response and the inputs is unknown, then the purpose is to use designs that spread the points at which the response is observed evenly throughout the region. These designs are called Space-Filling Designs (SFD) and the most known are Latin Hypercubes (random, orthogonal, optimized) and low discrepancy sequences. But, simulation codes becoming more and more complex, high dimensional optimal designs are needed to study a high number of parameters (more than 20 parameters) and the construction proves difficult. The aim of this study is to explore a construction method of...
Independent components analysis applied to 3D-front-face fluorescence spectra of edible oils to study the antioxydant effect of Nigella sativa L. extract on the thermal stability of heated oils
25 June 2011, 21:23:09
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 25 June 2011
Faten, Ammari , Christophe B.Y., Cordella , Néziha, Boughanmi , Douglas N., Rutledge
Independent Components Analysis (ICA) is one of the most widely used methods for blind source separation. In this paper we use this technique to facilitate the analysis of 3D- front face fluorescence spectra and to evaluate the efficiency of Nigella seed extract as a natural antioxidant compared with butylated hydroxytoluene (BHT) during accelerated oxidation of edible vegetable oils at 120°C, 140°C, 170°C and 190°C.ICA has demonstrated its power to extract the most informative signals and thus to allow the interpretation of the differences observed in the corresponding IC scores between Control, BHT-spiked and Nigella-spiked samples.The results of the study clearly...
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 25 June 2011
Faten, Ammari , Christophe B.Y., Cordella , Néziha, Boughanmi , Douglas N., Rutledge
Independent Components Analysis (ICA) is one of the most widely used methods for blind source separation. In this paper we use this technique to facilitate the analysis of 3D- front face fluorescence spectra and to evaluate the efficiency of Nigella seed extract as a natural antioxidant compared with butylated hydroxytoluene (BHT) during accelerated oxidation of edible vegetable oils at 120°C, 140°C, 170°C and 190°C.ICA has demonstrated its power to extract the most informative signals and thus to allow the interpretation of the differences observed in the corresponding IC scores between Control, BHT-spiked and Nigella-spiked samples.The results of the study clearly...
A nonparametric approach based on a Markov like property for classification
24 June 2011, 21:29:44
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 24 June 2011
Eun Sug, Park , Clifford, Spiegelman , Jeongyoun, Ahn
We suggest a new approach for classification based on nonparametricly estimated likelihoods. Due to the scarcity of data in high dimensions, full nonparametric estimation of the likelihood functions for each population is impractical. Instead, we propose to build a class of estimated nonparametric candidate likelihood models based on a Markov property and to provide multiple likelihood estimates that are useful for guiding a classification algorithm. Our density estimates require only estimates of one and two-dimensional marginal distributions, which can effectively get around the curse of dimensionality problem. A classification algorithm based on those estimated likelihoods is presented. A modification to...
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 24 June 2011
Eun Sug, Park , Clifford, Spiegelman , Jeongyoun, Ahn
We suggest a new approach for classification based on nonparametricly estimated likelihoods. Due to the scarcity of data in high dimensions, full nonparametric estimation of the likelihood functions for each population is impractical. Instead, we propose to build a class of estimated nonparametric candidate likelihood models based on a Markov property and to provide multiple likelihood estimates that are useful for guiding a classification algorithm. Our density estimates require only estimates of one and two-dimensional marginal distributions, which can effectively get around the curse of dimensionality problem. A classification algorithm based on those estimated likelihoods is presented. A modification to...
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