Multiblock-Based Qualitative and Quantitative Spectral Calibration Analysis

In this article, an improved spectral calibration and statistical analysis approach is proposed. Having realized the multiplicity of underlying spectral characteristics and their different effects on the quality of interpretation and prediction, the major task lies in how to qualify and quantify the descriptor−quality relationship more meaningfully over different wavelength subspaces. The underlying spectral information can be explored more comprehensively by a spectral subspace separation and multiblock modeling strategy. Unlike the specific purpose of quality predictions over different spectral subspaces, systematic information in both descriptor and quality spaces is decomposed into different parts under the control of different between-set relationships. Closely related variations and irrelevant ones are discriminated and evaluated separately, where, in particular, the orthogonal variations, as important systematic information, are quantified in specific model parameters, even though they are not pred...

[1]  John F. MacGregor,et al.  Process monitoring and diagnosis by multiblock PLS methods , 1994 .

[2]  M. Forina,et al.  Iterative predictor weighting (IPW) PLS: a technique for the elimination of useless predictors in regression problems , 1999 .

[3]  Michael J. Piovoso,et al.  On unifying multiblock analysis with application to decentralized process monitoring , 2001 .

[4]  Bernd Schmidt,et al.  Combining process and spectroscopic data to improve batch modeling , 2006 .

[5]  V. Tomišić,et al.  Raman spectra of aqueous solutions of strong electrolytes: evolving-factor analysis , 2000 .

[6]  D. Kleinbaum,et al.  Applied Regression Analysis and Other Multivariate Methods , 1978 .

[7]  Xueguang Shao,et al.  A wavelength selection method based on randomization test for near-infrared spectral analysis , 2009 .

[8]  Tom Fearn,et al.  On orthogonal signal correction , 2000 .

[9]  X. Z. Wang,et al.  A New Approach to Near-Infrared Spectral Data Analysis Using Independent Component Analysis , 2001, J. Chem. Inf. Comput. Sci..

[10]  Dong Wang,et al.  Successive projections algorithm combined with uninformative variable elimination for spectral variable selection , 2008 .

[11]  A K Smilde,et al.  Influence of temperature on vibrational spectra and consequences for the predictive ability of multivariate models. , 1998, Analytical chemistry.

[12]  S. Wold,et al.  Orthogonal signal correction of near-infrared spectra , 1998 .

[13]  Johan Trygg Prediction and spectral profile estimation in multivariate calibration , 2004 .

[14]  T. W. Anderson An Introduction to Multivariate Statistical Analysis , 1959 .

[15]  G. Févotte,et al.  Control of polymer molecular weight using near infrared spectroscopy , 2004 .

[16]  Paul Geladi,et al.  Interactive variable selection (IVS) for PLS. Part 1: Theory and algorithms , 1994 .

[17]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[18]  Olof Svensson,et al.  An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra , 1998 .

[19]  W. Cai,et al.  A new regression method based on independent component analysis. , 2006, Talanta.

[20]  Age K. Smilde,et al.  Monitoring of Batch Processes using Spectroscopy , 2002 .

[21]  Alison J. Burnham,et al.  Frameworks for latent variable multivariate regression , 1996 .

[22]  Chunhui Zhao,et al.  Spectra data analysis and calibration modeling method using spectra subspace separation and multiblock independent component regression strategy , 2011 .

[23]  F. Melgani,et al.  Multiple regression systems for spectrophotometric data analysis , 2009 .

[24]  Chunhui Zhao,et al.  Phase-Based Joint Modeling and Spectroscopy Analysis for Batch Processes Monitoring , 2010 .

[25]  Jean-Michel Roger,et al.  Robust calibration using orthogonal projection and experimental design. Application to the correction of the light scattering effect on turbid NIR spectra , 2008 .

[26]  Honglu Yu,et al.  Post processing methods (PLS–CCA): simple alternatives to preprocessing methods (OSC–PLS) , 2004 .

[27]  P. H. Araújo,et al.  Spectroscopic on-line monitoring of reactions in dispersed medium: chemometric challenges. , 2007, Analytica chimica acta.

[28]  Yudi Pawitan,et al.  Variable selection in random calibration of near‐infrared instruments: ridge regression and partial least squares regression settings , 2003 .

[29]  Romà Tauler,et al.  Monitoring and modeling of protein processes using mass spectrometry, circular dichroism, and multivariate curve resolution methods. , 2006, Analytical chemistry.

[30]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[31]  Age K. Smilde,et al.  Rapid estimation of rate constants of batch processes using on-line SW-NIR , 1998 .

[32]  Rolf Ergon,et al.  Reduced PCR/PLSR models by subspace projections , 2006 .

[33]  Richard G. Brereton,et al.  Introduction to multivariate calibration in analytical chemistry , 2000 .

[34]  J. Macgregor,et al.  Analysis of multiblock and hierarchical PCA and PLS models , 1998 .

[35]  E. Martin,et al.  Gaussian process regression for multivariate spectroscopic calibration , 2007 .

[36]  J. Neter,et al.  Applied Linear Regression Models , 1983 .

[37]  Theodora Kourti,et al.  Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS , 1995 .

[38]  A. Smilde,et al.  Multiblock PLS analysis of an industrial pharmaceutical process , 2002, Biotechnology and bioengineering.

[39]  Smilde,et al.  Spectroscopic monitoring of batch reactions for on-line fault detection and diagnosis , 2000, Analytical chemistry.

[40]  Tom Fearn,et al.  Transfer by orthogonal projection: making near-infrared calibrations robust to between-instrument variation , 2004 .

[41]  Age K. Smilde,et al.  Direct orthogonal signal correction , 2001 .

[42]  Elaine Martin,et al.  Bayesian linear regression and variable selection for spectroscopic calibration. , 2009, Analytica chimica acta.

[43]  L. G. Blackwood Factor Analysis in Chemistry (2nd Ed.) , 1994 .

[44]  Bayesian regularization: application to calibration in NIR spectroscopy , 2009 .

[45]  In-Beum Lee,et al.  Fault detection and diagnosis based on modified independent component analysis , 2006 .