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Hanafi M.,Unite de Sensometrie et de Chimiometrie | Kohler A.,Center for Biospectroscopy and Data Modelling | Qannari E.-M.,Unite de Sensometrie et de Chimiometrie
Chemometrics and Intelligent Laboratory Systems | Year: 2011

Consensus Principal Component Analysis is a multiblock method which is designed to reveal covariant patterns between and within several multivariate data sets. The computation of the parameters of this method namely, block scores, block loadings, global loadings and global scores are based on an iterative procedure. However, very few properties are known regarding the convergence of this iterative procedure. The paper discloses a monotony property of CPCA and exhibits an optimisation criterion for which CPCA algorithm provides a monotonic convergent solution. This makes it possible to highlight new properties of this method of analysis and pinpoint its connection to existing methods such as Generalized Canonical Correlation Analysis and Multiple Co-inertia Analysis. © 2010. Source


Hanafi M.,Oniris | Hanafi M.,University of Nantes | Kohler A.,Center for Biospectroscopy and Data Modelling | Qannari E.M.,Oniris | Qannari E.M.,University of Nantes
Journal of Chemometrics | Year: 2010

Hierarchical Principal Component Analysis (HPCA) is a multiblock method which is designed to reveal covariant patterns between and within several multivariate datasets. The computation of the parameters of this method, namely block scores, block loadings, global loadings and global scores, is based on an iterative procedure. However, very few properties are known regarding the convergence of this iterative procedure. The paper discloses a monotony property of HPCA and exhibits an optimization criterion for which HPCA algorithm provides a monotonic convergent solution. This makes it possible to shed a new light on this method of analysis by showing new properties and pinpointing its relation to existing methods such as Common Component and Specific Weights Analysis (CCSWA), INDSCAL and PARAFAC Models. © 2010 John Wiley & Sons, Ltd. Source


Zimmermann B.,Ruder Boskovic Institute | Kohler A.,Norwegian University of Life Sciences | Kohler A.,Center for Biospectroscopy and Data Modelling
Applied Spectroscopy | Year: 2013

Calculating derivatives of spectral data by the Savitzky-Golay (SG) numerical algorithm is often used as a preliminary preprocessing step to resolve overlapping signals, enhance signal properties, and suppress unwanted spectral features that arise due to nonideal instrument and sample properties. Addressing these issues, a study of the simulated and measured infrared data by partial least-squares regression has been conducted. The simulated data sets were modeled by considering a range of undesired chemical and physical spectral anomalies and variations that can occur in a measured spectrum, such as baseline variations, noise, and scattering effects. The study has demonstrated the importance of the optimization of the SG parameters during the conversion of spectra into derivative form, specifically window size and polynomial order of the fitting curve. A specific optimal window size is associated with an exact component of the system being estimated, and this window size does not necessarily apply for some other component present in the system. Since the optimization procedure can be time-consuming, as a rough guideline spectral noise level can be used for assessment of window size. Moreover, it has been demonstrated that, when the extended multiplicative signal correction (EMSC) is used alongside the SG procedure, the derivative treatment of data by the SG algorithm must precede the EMSC normalization. © 2013 Society for Applied Spectroscopy. Source


Bassan P.,University of Manchester | Kohler A.,Center for Biospectroscopy and Data Modelling | Martens H.,Center for Biospectroscopy and Data Modelling | Martens H.,Norwegian University of Life Sciences | And 7 more authors.
Journal of Biophotonics | Year: 2010

In the field of biomedical infrared spectroscopy it is often desirable to obtain spectra at the cellular level. Samples consisting of isolated single biological cells are particularly unsuited to such analysis since cells are strong scatterers of infrared radiation. Thus measured spectra consist of an absorption component often highly distorted by scattering effects. It is now known that the predominant contribution to the scattering is Resonant Mie Scattering (RMieS) and recently we have shown that this can be corrected for, using an iterative algorithm based on Extended Multiplicative Signal Correction (EMSC) and a Mie approximation formula. Here we present an iterative algorithm that applies full Mie scattering theory. In order to avoid noise accumulation in the iterative algorithm a curve-fitting step is implemented on the new reference spectrum. The new algorithm increases the computational time when run on an equivalent processor. Therefore parallel processing by a Graphics Processing Unit (GPU) was employed to reduce computation time. The optimised RMieS-EMSC algorithm is applied to an IR spectroscopy data set of cultured single isolated prostate cancer (PC-3) cells, where it is show that spectral distortions from RMieS are removed. © 2010 by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source


Bassan P.,University of Manchester | Kohler A.,Center for Biospectroscopy and Data Modelling | Kohler A.,Norwegian University of Life Sciences | Martens H.,Center for Biospectroscopy and Data Modelling | And 8 more authors.
Analyst | Year: 2010

Infrared spectra of single biological cells often exhibit the 'dispersion artefact' observed as a sharp decrease in intensity on the high wavenumber side of absorption bands, in particular the Amide I band at ∼1655 cm -1, causing a downward shift of the true peak position. The presence of this effect makes any biochemical interpretation of the spectra unreliable. Recent theory has shed light on the origins of the 'dispersion artefact' which has been attributed to resonant Mie scattering (RMieS). In this paper a preliminary algorithm for correcting RMieS is presented and evaluated using simulated data. Results show that the 'dispersion artefact' appears to be removed; however, the correction is not perfect. An iterative approach was subsequently implemented whereby the reference spectrum is improved after each iteration, resulting in a more accurate correction. Consequently the corrected spectra become increasingly more representative of the pure absorbance spectra. Using this correction method reliable peak positions can be obtained. © 2010 The Royal Society of Chemistry. Source

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