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C. Multivariate Data Analysis

Ship fuel consumption monitoring and fault detection via partial least squares and control charts of navigation data. Transportation Research Part D: Transport and Environment.
Capezza C, Coleman, SY, Lepore A, Palumbo B, Vitiello L.
in press, 2019

C1 Multivariate data analysis

A comparative investigation of the combined effects of pre processing, wavelength selection and regression methods on near-infrared calibration model performance
Wan Jian, Chen Yi-Chieh, Morris A Julian, Thennadil N Suresh
Appl. Spectrosc., 2017, On-Line 30/03/17
A comparative investigation of the combined effects of pre-processing, wavelength selection and regression methods on near infrared calibration model performance,
Wan J., Chen, Y.-C., Morris, J. A. and Thennadil, S. N
Applied Spectroscopy, 2017, Jul:71 (7), 1432-1446
Calibration of multiplexed fibre optic spectroscopy
Chen ZP, Zhong LJ, Nordon A, Littlejohn D, Holden M, Fazenda M, Harvey L, McNeil B, Faulkner J and Morris J
Analytical Chemistry, 2011, 83, 2655-2659
Classifying with confidence using Bayes rule and kernel density estimation
Fearn T, Perez Marin D, Garrido Varo A, Guerrero Ginel J E
Chemometrics and Intelligent Laboratory Systems, 2019, doi: 10.1016/j.chemlab.2019.04.0004
Comparison of partial least squares regression, least squares support vector machines and Gaussian process regression for a near infrared calibration
Cui C and Fearn T
Journal of Near Infrared Spectroscopy, 2017, 25(1), 5 - 14
Determination of optimum number of components in partial least squares regression from distributions of the root-mean-squared error obtained by Monte Carlo resampling
Kvalheim O M; Arneberg R; Grung B; Rajalahti T
Chemometrics, 2018, 32 e2993
Effect of particle size distribution on spatially and angularly resolved diffuse reflectance measurement
Chen Yi-Chieh, Tiernan-Vandermotten Sarra, Lue Leo, Ferreira Carla Sofia, Sefcik Jan, Thannadil Suresh
European Pharamaceutical Review, 2018, 23, 34-37
Gaussian process regression for multivariate spectroscopic calibration
Chen T, Morris J and Martin E
Chemometrics and Intelligent Laboratory Systems, 2007, 87, 59-71
Hierarchical mixture of linear regressions for multivariate spectroscopic calibration: An application for NIR calibration.
Cui C, Fearn T
Chemometrics and Intelligent Laboratory Systems, 2018, doi:10.1016/j.chemolab.217.12.013
Image analysis framework with focus evaluation for in situ characterisation of particle size and shape attributes
Cardona Javier, Ferreira Carla, McGinty John, Hamilton, Andrew, Agimelen Okpeafoh S, Cleary Alison, Atkinson Robert, Michie Craig, Marshall Stephen, Chen Yi-Chieh, Sefcik Jan, Andonovic Ivan, Tachtatzis Christos
Chemical Engineering Science, 2018, Vol 191, 208-231
Insight from data analytics with an automotive aftermarket SME
Smith W, Coleman S, Bacardit J, Coxon S
Quality and Reliability Engineering International, 2019
Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration
Cui C and Fearn T
Chemometrics and Intelligent Laboratory Systems, 2018, doi:10.1016/j.chemolab.2018.07.008
Number of components and prediction error in partial least squares regression determined by Monte Carlo resampling strategies
Kvalheim O M; Grung B; Rajalahti T
Chemometrics and Intelligent Laboratory Systems, 2019, 188, 79-86
Process analytical technology and compensating for nonlinear effects in process spectroscopic data for improved process monitoring and control
Chen Z and Morris J
Biotechnology J., 2009, 4(5), 610-619
Quantitative analysis of powder mixtures by Raman spectrometry: the influence of particle size and its correction
Chen Z, Li L, Jin J, Nordon A, Littlejohn D, Yang J, Zhang J and Yu R
Analytical Chemistry, 2012, 84, 4088-4094
Spatially and angularly resolved spectroscopy for in-situ estimation of concentration and particle size in colloidal suspensions
Chen Yi-Chieh, Foo David, Dehanov Nicolau, Thennadil Suresh N
Analytical and Bioanalytical Chemistry, 2017, 409, 6975-6988

C2 Calibration transfer/model maintenance

An advanced calibration strategy for in situ quantitative monitoring of solvent-mediated phase transition processes using FT-Raman spectroscopy
Chen Z-P, Fevotte G, Littlejohn D and Morris A J
Analytical Chemistry, 2008, 80, 6658-6665
Calibration of multiplexed fibre optic spectroscopy
Chen ZP, Zhong LJ, Nordon A, Littlejohn D, Holden M, Fazenda M, Harvey L, McNeil B, Faulkner J and Morris J
Analytical Chemistry, 2011, 83, 2655-2659
Fermentation process tracking through enhanced spectral calibration modeling
Triadaphillou S, Martin E, Montague G, Nordon A, Jeffkins P and Stimpson S
Biotechnol. Bioeng., 2007, 97, 554-567
Gaussian process regression for multivariate spectroscopic calibration
Chen T, Morris J and Martin E
Chemometrics and Intelligent Laboratory Systems, 2007, 87, 59-71
Maintaining the predictive abilities of multivariate calibration models by spectral space transformation
Du W, Chen ZP, Zhong LJ, Wang SX, Yu RQ, Nordon A, Littlejohn D and Holden M
Analytica Chimica Acta, 2011, 690, 64-70
Maintenance of a calibration model for near infrared spectrometry by a combined principal component analysis/partial least squares approach
Setarehdan S K, Soraghan J J, Littlejohn D, Sadler D A
Analytica Chimica Acta,, 2002, 452, 35-45
Systematic prediction error correction: A novel strategy for maintaining the predictive abilities of multivariate calibration models
Chen ZP, Li LM, Yu RQ, Littlejohn D, Nordon A, Morris AJ, Dann AS, Jeffkins PA, Richardson MD and Stimpson SL
Analyst, 2011, 136, 98-106
Variable selection for PLS calibration of NIR data from othogonally designed experiements
Setarehdan S K, Soraghan J J, Littlejohn D, Sadler D A
Applied Spectroscopy, 2002, 56, 337-345