C. Abrahamsson, . Johansson, F. Sparen, and . Lindgren, Comparison of different variable selection methods conducted on NIR transmission measurements on intact tablets, Chemometrics and Intelligent Laboratory Systems, vol.69, issue.1-2, pp.3-12, 2003.
DOI : 10.1016/S0169-7439(03)00064-9

M. C. Araújo, T. C. Saldanha, R. K. Galvão, H. Yoneyama, . Chame et al., The successive projections algorithm for variable selection in spectroscopic multicomponent analysis, Chemometrics and Intelligent Laboratory Systems, vol.57, issue.2, pp.65-73, 2001.
DOI : 10.1016/S0169-7439(01)00119-8

N. Aske, H. Kallevik, and J. Sjöblom, Determination of Saturate, Aromatic, Resin, and Asphaltenic (SARA) Components in Crude Oils by Means of Infrared and Near-Infrared Spectroscopy, Energy & Fuels, vol.15, issue.5, pp.1304-1312, 2001.
DOI : 10.1021/ef010088h

R. J. Barnes, M. Dhanoa, and S. J. Lister, Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra, Applied Spectroscopy, vol.43, issue.5, pp.737-891, 1989.
DOI : 10.1366/0003702894202201

K. Baumann, H. Albert, and V. Korff, A systematic evaluation of the benefits and hazards of variable selection in latent variable regression. Part I. Search algorithm, theory and simulations, Journal of Chemometrics, vol.33, issue.7, pp.339-350, 2002.
DOI : 10.1002/cem.730

D. Beasley, D. Bull, and R. Martin, An overview of genetic algorithms : Part 1, fundamentals, pp.58-59, 1993.

D. Beasley, D. Bull, and R. Martin, An overview of genetic algorithms : Part 2, research topics, pp.170-181, 1993.

K. Beebe and B. Kowalski, An Introduction to Multivariate Calibration and Analysis, Analytical Chemistry, vol.59, issue.17, p.1007, 1987.
DOI : 10.1021/ac00144a725

J. Beens and U. A. Brinkman, The role of gas chromatography in compositional analyses in the petroleum industry, TrAC Trends in Analytical Chemistry, vol.19, issue.4, pp.260-275, 2000.
DOI : 10.1016/S0165-9936(99)00205-8

A. Berglund and S. Wold, A serial extension of multiblock PLS, Journal of Chemometrics, vol.16, issue.3-4, pp.461-471, 1999.
DOI : 10.1002/(SICI)1099-128X(199905/08)13:3/4<461::AID-CEM555>3.0.CO;2-B

D. Bertrand and E. Dufour, La spectroscopie infrarouge, 2006.

M. Blanco, . Maspoch, . Villarroya, J. Peralta, J. Gonzalez et al., Determination of the penetration value of bitumens by near infrared spectroscopy, The Analyst, vol.125, issue.10, pp.1823-1828, 2000.
DOI : 10.1039/b004121l

M. Blanco, . Maspoch, . Villarroya, J. Peralta, J. Gonzalez et al., Determination of physical properties of bitumens by use of near-infrared spectroscopy with neural networks. Joint modelling of linear and non-linear parameters, The Analyst, vol.126, issue.3, pp.378-382, 2001.
DOI : 10.1039/b009255j

M. Blanco, . Maspoch, . Villarroya, J. Peralta, J. Gonzalez et al., Determination of physico-chemical parameters for bitumens using near infrared spectroscopy, Analytica Chimica Acta, vol.434, issue.1, pp.133-141, 2001.
DOI : 10.1016/S0003-2670(01)00811-X

Z. Boger, Selection of quasi-optimal inputs in chemometrics modeling by artificial neural network analysis, Analytica Chimica Acta, vol.490, issue.1-2, pp.31-40, 2003.
DOI : 10.1016/S0003-2670(03)00349-0

C. W. Brown, P. Lynch, R. Obremski, and D. Lavery, Matrix representations and criteria for selecting analytical wavelengths for multicomponent spectroscopic analysis, Analytical Chemistry, vol.54, issue.9, pp.1472-1479, 1982.
DOI : 10.1021/ac00246a007

J. Pb-bràs, S. Bernardino, J. Lopes, and . Menezes, Multiblock pls as an approach to compare and combine nir and mir spectra in calibrations of soybean flour. Chemometrics and Intelligent Laboratory Systems, pp.91-99, 2005.

V. Centner, D. Massart, O. De-jong, B. Vandeginste, and C. Sterna, Elimination of Uninformative Variables for Multivariate Calibration, Analytical Chemistry, vol.68, issue.21, pp.683851-3858, 1996.
DOI : 10.1021/ac960321m

V. Centner, L. Verdu-andres, D. Walczak, F. Jouan-rimbaud, . Despagne et al., Comparison of Multivariate Calibration Techniques Applied to Experimental NIR Data Sets, Applied Spectroscopy, vol.54, issue.4, pp.608-622, 2000.
DOI : 10.1366/0003702001949816

D. Chen, W. Cai, and X. Shao, Representative subset selection in modified iterative predictor weighting (mIPW) ??? PLS models for parsimonious multivariate calibration, Chemometrics and Intelligent Laboratory Systems, vol.87, issue.2, pp.312-318, 2007.
DOI : 10.1016/j.chemolab.2007.04.001

H. Chung, Applications of Near???Infrared Spectroscopy in Refineries and Important Issues to Address, Applied Spectroscopy Reviews, vol.48, issue.3, pp.251-285, 2007.
DOI : 10.1016/S0003-2670(03)00349-0

H. Chung and M. S. Ku, Comparison of Near-Infrared, Infrared, and Raman Spectroscopy for the Analysis of Heavy Petroleum Products, Applied Spectroscopy, vol.54, issue.2, pp.239-245, 2000.
DOI : 10.1366/0003702001949168

H. Chung and M. S. Ku, Near-Infrared Spectroscopy for On-Line Monitoring of Lube Base Oil Processes, Applied Spectroscopy, vol.57, issue.5, pp.545-550, 2003.
DOI : 10.1366/000370203321666579

G. Cruciani, . Baroni, . Clementi, D. Costantino, . Riganelli et al., Predictive ability of regression models. Part I: Standard deviation of prediction errors (SDEP), Journal of Chemometrics, vol.14, issue.6, pp.335-346, 1992.
DOI : 10.1002/cem.1180060604

P. J. De-groot, G. Postma, W. J. Melssen, and L. M. Buydens, Selecting a representative training set for the classification of demolition waste using remote NIR sensing, Analytica Chimica Acta, vol.392, issue.1, pp.67-75, 1999.
DOI : 10.1016/S0003-2670(99)00193-2

L. F. De-lira, M. S. De-albuquerque, J. G. Pacheco, T. Fonseca, E. H. Cavalcanti et al., Infrared spectroscopy and multivariate calibration to monitor stability quality parameters of biodiesel, Microchemical Journal, vol.96, issue.1, pp.126-131, 2010.
DOI : 10.1016/j.microc.2010.02.014

P. De-peinder, D. Visser, . Petrauskas, . Salvatori, B. Soulimani et al., Partial least squares modeling of combined infrared, 1H NMR and 13C NMR spectra to predict long residue properties of crude oils, Vibrational Spectroscopy, vol.51, issue.2, pp.205-212, 2009.
DOI : 10.1016/j.vibspec.2009.04.009

D. Deaven and K. Ho, Molecular Geometry Optimization with a Genetic Algorithm, Physical Review Letters, vol.75, issue.2, pp.288-291, 1995.
DOI : 10.1103/PhysRevLett.75.288

O. Devos and L. Duponchel, Parallel genetic algorithm co-optimization of spectral pre-processing and wavelength selection for PLS regression, Chemometrics and Intelligent Laboratory Systems, vol.107, issue.1, pp.50-58, 2011.
DOI : 10.1016/j.chemolab.2011.01.008

URL : https://hal.archives-ouvertes.fr/hal-00593275

A. Doucet, N. De-freitas, and N. Gordon, Sequential Monte Carlo methods in practice, 2001.
DOI : 10.1007/978-1-4757-3437-9

Y. Du, Y. Liang, J. Jiang, R. Berry, and . Ozaki, Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares, Analytica Chimica Acta, vol.501, issue.2
DOI : 10.1016/j.aca.2003.09.041

A. Durand, Méthodes de sélection de variables appliquées en spectroscopie proche infrarouge pour l'analyse et la classification de textiles, Thèse de doctorat, Université des sciences et technologies de Lille -Ecole doctorale des sciences pour l'ingénieur, 2007.

A. Durand, C. Devos, J. Ruckebusch, and . Huvenne, Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton???viscose textiles, Analytica Chimica Acta, vol.595, issue.1-2, pp.72-79, 2007.
DOI : 10.1016/j.aca.2007.03.024

URL : https://hal.archives-ouvertes.fr/hal-00267224

B. Efron, Bootstrap Methods: Another Look at the Jackknife, The Annals of Statistics, vol.7, issue.1, pp.1-26, 1979.
DOI : 10.1214/aos/1176344552

B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap, 1993.
DOI : 10.1007/978-1-4899-4541-9

P. Eilers, J. Marx, . Eyssautier, . Levitz, . Espinat et al., Flexible smoothing with b-splines and penalties Insight into asphaltene nanoaggregate structure inferred by small angle neutron and x-ray scattering, Statistical Science Journal of physical chemistry B, vol.11, issue.115, pp.89-1216827, 1996.

C. Felicio, L. Bràs, J. Lopes, L. Cabrita, and J. Menezes, Comparison of pls algorithms in gasoline and monitoring with mir and nir. Chemometrics and Intelligent Laboratory System, pp.74-80, 2005.

R. K. Galvão, M. C. Araujo, G. José, M. J. Pontes, E. Da-silva et al., A method for calibration and validation subset partitioning, Talanta, vol.67, issue.4
DOI : 10.1016/j.talanta.2005.03.025

P. Geladi and B. Kowalski, Partial least-squares regression: a tutorial, Analytica Chimica Acta, vol.185, pp.19-32, 1986.
DOI : 10.1016/0003-2670(86)80028-9

P. Geladi and . Martens, Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat, Applied Spectroscopy, vol.39, issue.3, pp.491-500, 1985.
DOI : 10.1366/0003702854248656

W. Gilbert, F. Gusmao-de-lima, and A. Bueno, Comparison of NIR and NMR spectra chemometrics for FCC feed online characterization, Studies in Surface Science and Catalysis, vol.149, 2004.
DOI : 10.1016/S0167-2991(04)80764-X

G. Giskeodegard, M. Grinde, D. Sitter, . Axelson, . Lundgren et al., Multivariate Modeling and Prediction of Breast Cancer Prognostic Factors Using MR Metabolomics, Journal of Proteome Research, vol.9, issue.2, pp.972-979, 2010.
DOI : 10.1021/pr9008783

D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, 1989.

C. Grinstead and J. Snell, Introduction to probability, 1997.

D. Haaland and E. Thomas, Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information, Analytical Chemistry, vol.60, issue.11, pp.601193-1202, 1988.
DOI : 10.1021/ac00162a020

L. Hadjiiski, P. Geladi, and P. Hopke, A comparison of modeling nonlinear systems with artificial neural networks and partial least squares, Chemometrics and Intelligent Laboratory Systems, vol.49, issue.1, pp.91-103, 1999.
DOI : 10.1016/S0169-7439(99)00030-1

A. Hannisdal, P. I. Hemmingsen, and J. Sjöblom, Group-Type Analysis of Heavy Crude Oils Using Vibrational Spectroscopy in Combination with Multivariate Analysis, Industrial & Engineering Chemistry Research, vol.44, issue.5, pp.1349-1357, 2005.
DOI : 10.1021/ie0401354

K. Hidajat and S. Chong, Quality characterisation of crude oils by partial least square calibration of NIR spectral profiles, Journal of Near Infrared Spectroscopy, vol.8, issue.1, pp.53-59, 2000.
DOI : 10.1255/jnirs.264

J. Holland, Adaptation in natural and artificial systems : an introductory analysis with applications to biology, control, and artificial intelligence, 1992.

Y. Hongfu, C. Xiaoli, L. Hoaran, and X. Yupeng, Determination of multi-properties of residual oils using mid-infrared attenuated total reflection spectroscopy, Fuel, vol.85, issue.12-13, pp.1720-1728, 2006.
DOI : 10.1016/j.fuel.2006.02.003

L. Ingber, Very fast simulated re-annealing, Mathematical and Computer Modelling, vol.12, issue.8, pp.967-973, 1989.
DOI : 10.1016/0895-7177(89)90202-1

URL : http://doi.org/10.1016/0895-7177(89)90202-1

D. Jouan-rimbaud, D. Massart, R. Leardi, and O. De-noord, Genetic Algorithms as a Tool for Wavelength Selection in Multivariate Calibration, Analytical Chemistry, vol.67, issue.23, pp.4295-4301, 1995.
DOI : 10.1021/ac00119a015

D. Jouan-rimbaud, D. Massart, and C. Saby, Characterisation of the representativity of selected sets of samples in multivariate calibration and pattern recognition, Analytica Chimica Acta, vol.350, issue.1-2, pp.149-161, 1997.
DOI : 10.1016/S0003-2670(97)00296-1

J. Kalivas, N. Roberts, J. Sutter, S. Kasemsumran, Y. Du et al., Global optimization by simulated annealing with wavelength selection for ultraviolet-visible spectrophotometry Improvement of partial least squares models for in vitro and in vivo glucose quantifications by using near-infrared spectroscopy and searching combination moving window partial least squares, Analytical Chemistry, vol.59, issue.82, pp.612024-203097, 1989.

R. Kennard and L. A. Stone, Computer Aided Design of Experiments, Technometrics, vol.11, issue.1, pp.137-148, 1969.
DOI : 10.1080/00401706.1969.10490666

S. Kirkpatrick and M. P. Vecchi, Optimization by Simulated Annealing, Science, vol.220, issue.4598, pp.671-680, 1983.
DOI : 10.1126/science.220.4598.671

J. Koljonen, T. E. Nordling, and J. Alander, A review of genetic algorithms in near infrared spectroscopy and chemometrics: past and future, Journal of Near Infrared Spectroscopy, vol.16, issue.1, pp.189-197, 2008.
DOI : 10.1255/jnirs.778

T. Kourti, P. Nomikos, and J. Macgregor, Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS, Journal of Process Control, vol.5, issue.4, pp.277-284, 1995.
DOI : 10.1016/0959-1524(95)00019-M

R. Leardi, Genetic algorithms in chemometrics and chemistry: a review, Journal of Chemometrics, vol.348, issue.7, pp.559-569, 2001.
DOI : 10.1002/cem.651

R. Leardi, Genetic algorithms in chemistry, Journal of Chromatography A, vol.1158, issue.1-2, pp.226-233, 2007.
DOI : 10.1016/j.chroma.2007.04.025

R. Leardi, R. Boggia, and M. Terrile, Genetic algorithms as a strategy for feature selection, Journal of Chemometrics, vol.16, issue.5, pp.267-281, 1992.
DOI : 10.1002/cem.1180060506

R. Leardi and M. Lupiáñez, Genetic algorithms applied to feature selection in pls regression : how and when to use them. Chemometrics and intelligent laboratory systems, pp.5-6195, 1998.

R. Leardi and L. Nørgaard, Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions, Journal of Chemometrics, vol.446, issue.11, pp.486-497, 2004.
DOI : 10.1002/cem.893

F. S. Lima, M. A. Araùjo, and L. E. Borges, Determination of lubricant base oil properties by near infrared spectroscopy using different sample and variable selection methods, Journal of Near Infrared Spectroscopy, vol.12, issue.1, pp.159-166, 2004.
DOI : 10.1255/jnirs.422

F. S. Lima and L. F. Leite, Determination of Asphalt Cement Properties by Near Infrared Spectroscopy and Chemometrics, Petroleum Science and Technology, vol.22, issue.5-6, pp.589-600, 2004.
DOI : 10.1016/S0169-7439(98)00075-6

W. Lindberg, J. Persson, and S. Wold, Partial least-squares method for spectrofluorimetric analysis of mixtures of humic acid and lignin sulfonate, Analytical Chemistry, vol.55, issue.4, pp.643-648, 1983.
DOI : 10.1021/ac00255a014

Y. Long, H. Dabros, and . Hamza, Analysis of Solvent-Diluted Bitumen from Oil Sands Froth Treatment Using NIR Spectroscopy, The Canadian Journal of Chemical Engineering, vol.80, issue.4, pp.776-781, 2004.
DOI : 10.1002/cjce.5450820416

J. Lopes, J. Menezes, J. A. Westerhuis, and A. Smilde, Multiblock PLS analysis of an industrial pharmaceutical process, Biotechnology and Bioengineering, vol.15, issue.4, pp.419-427, 2002.
DOI : 10.1002/bit.10382

J. Macgregor, C. Jaeckle, M. Kiparissides, and . Koutoudi, Process monitoring and diagnosis by multiblock PLS methods, AIChE Journal, vol.40, issue.5, pp.826-838, 1994.
DOI : 10.1002/aic.690400509

H. Mark, Comparative study of calibration methods for near-infrared reflectance analysis using a nested experimental design, Analytical Chemistry, vol.58, issue.13, pp.2814-2819, 1986.
DOI : 10.1021/ac00126a051

H. Martens, S. Jensen, and P. Geladi, Multivariate linearity transformations for near infrared reflectance spectroscopy, Applied Statistics, pp.205-234, 1983.

H. Martens and T. , Naes : Multivariate Calibration, 1989.

A. Mckenna, J. Purcell, R. Rodgers, and A. Marshall, Heavy Petroleum Composition. 1. Exhaustive Compositional Analysis of Athabasca Bitumen HVGO Distillates by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry: A Definitive Test of the Boduszynski Model, Energy & Fuels, vol.24, issue.5, pp.2929-2938, 2010.
DOI : 10.1021/ef100149n

I. Merdrignac and D. Espinat, Physicochemical characterization of petroleum fractions : the state of the art. Oil & Gas Science and Technology -Rev, IFP, vol.62, issue.1, pp.7-32, 2007.

O. Mullins, J. Murgich, J. Rodriguez, and Y. Aray, Asphaltenes in crude oil : absorbers and/or scatterers in the nearinfrared region ? Analytical Chemistry Molecular recognition and molecular mechanics of micelles of some model asphaltenes and resins, Energy & Fuels, vol.6281, issue.10, pp.508-51468, 1990.

K. Nielsen, . Dittmer, N. C. Malmendal, and . Nielsen, H Nuclear Magnetic Resonance (NMR) Spectroscopy and Multivariate Data Analysis, Energy & Fuels, vol.22, issue.6, pp.4070-4076, 2008.
DOI : 10.1021/ef800539g

A. S. Nørgaard, J. Wagner, L. Nielsen, S. Munck, and . Engelsen, Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy, Applied Spectroscopy, vol.54, issue.3, pp.413-419, 2000.
DOI : 10.1366/0003702001949500

B. Osborne and T. Fearn, Near-Infrared Spectroscopy in Food Analysis, Longman Scientific & Technical, vol.4, 1986.
DOI : 10.1002/9780470027318.a1018

B. Osborne, T. Fearn, and P. Hindle, Practical NIR spectroscopy with applications in food and beverage analysis, 1993.

C. Pasquini and A. Bueno, Characterization of petroleum using near-infrared spectroscopy: Quantitative modeling for the true boiling point curve and specific gravity, Fuel, vol.86, issue.12-13, pp.1927-1934, 2007.
DOI : 10.1016/j.fuel.2006.12.026

R. Poilblanc and F. Crasnier, Spectroscopies infrarouge et Raman, EDP Sciences, 2006.

A. Rinnan, . Van-den, . Berg, and . Balling-engelsen, Review of the most common pre-processing techniques for near-infrared spectra, TrAC Trends in Analytical Chemistry, vol.28, issue.10, pp.1201-1222, 2009.
DOI : 10.1016/j.trac.2009.07.007

L. Riveros, M. A. Jaimes, J. Ranaudo, J. Castillo, and . Chirinos, Determination of Asphaltene and Resin Content in Venezuelan Crude Oils by Using Fluorescence Spectroscopy and Partial Least Squares Regression, Energy & Fuels, vol.20, issue.1, pp.227-230, 2006.
DOI : 10.1021/ef0501243

Y. Roggo, C. Duponchel, J. Ruckebusch, and . Huvenne, Statistical tests for comparison of quantitative and qualitative models developed with near infrared spectral data, Journal of Molecular Structure, vol.654, issue.1-3, pp.253-262, 2003.
DOI : 10.1016/S0022-2860(03)00248-5

URL : https://hal.archives-ouvertes.fr/hal-00275352

B. Rosner, M. L. Glynn, and R. Lee, The Wilcoxon Signed Rank Test for Paired Comparisons of Clustered Data, Biometrics, vol.6, issue.1, pp.185-192, 2006.
DOI : 10.1111/j.1541-0420.2005.00389.x

C. Ruckebusch, . Orhan, T. Durand, J. Boubellouta, and . Huvenne, Quantitative Analysis of Cotton???Polyester Textile Blends from Near-Infrared Spectra, Applied Spectroscopy, vol.60, issue.5
DOI : 10.1366/000370206777412194

URL : https://hal.archives-ouvertes.fr/hal-00267612

S. Satya, R. Roehner, M. Deo, and F. Hanson, Estimation of Properties of Crude Oil Residual Fractions Using Chemometrics, Energy & Fuels, vol.21, issue.2, 2007.
DOI : 10.1021/ef0601420

A. Savitsky and M. J. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures., Analytical Chemistry, vol.36, issue.8, pp.1627-1639, 1964.
DOI : 10.1021/ac60214a047

J. Snyman, Practical Mathematical Optimization : An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms, 2005.

J. Speight, Petroleum asphaltenes -part 1 -asphaltenes, resins and the structure of petroleum. Oil & Gas Science and Technology -Rev, IFP, vol.59, issue.5, pp.467-477, 2004.

. Cote, Theoretical justification of wavelength selection in pls calibration : Development of a new algorithm, Analytical Chemistry, vol.70, pp.35-44, 1998.

H. Swierenga, O. Wülfert, A. De-noord, A. De-weijer, L. M. Smilde et al., Development of robust calibration models in near infra-red spectrometric applications, Analytica Chimica Acta, vol.411, issue.1-2, pp.121-135, 2000.
DOI : 10.1016/S0003-2670(00)00718-2

H. Szu and R. Hartley, Fast simulated annealing, Physics Letters A, vol.122, issue.3-4, pp.157-162, 1987.
DOI : 10.1016/0375-9601(87)90796-1

M. L. Thompson, Selection of Variables in Multiple Regression: Part I. A Review and Evaluation, International Statistical Review / Revue Internationale de Statistique, vol.46, issue.1, pp.1-19, 1978.
DOI : 10.2307/1402505

R. Todeschini, J. Galvagni, . Vilchez, N. Olmo, and . Navas, Kohonen artificial neural networks as a tool for wavelength selection in multicomponent spectrofluorimetric PLS modelling: application to phenol, o-cresol, m-cresol and p-cresol mixtures, [103] H Van Der Voet : Comparing the predictive accuracy of models using a simple randomization test. Chemometrics and Intelligent Laboratory System, pp.93-98313, 1994.
DOI : 10.1016/S0165-9936(98)00097-1

R. Wang, C. Lin, and J. Lin, Image hiding by optimal LSB substitution and genetic algorithm, Pattern Recognition, vol.34, issue.3, pp.671-683, 2001.
DOI : 10.1016/S0031-3203(00)00015-7

L. E. Wangen and B. Kowalski, A multiblock partial least squares algorithm for investigating complex chemical systems, Journal of Chemometrics, vol.185, issue.1, pp.3-20, 1988.
DOI : 10.1002/cem.1180030104

J. Westerhuis and P. M. Coenegracht, Multivariate modelling of the pharmaceutical two-step process of wet granulation and tableting with multiblock partial least squares, Journal of Chemometrics, vol.11, issue.5, pp.379-392, 1997.
DOI : 10.1002/(SICI)1099-128X(199709/10)11:5<379::AID-CEM482>3.3.CO;2-#

J. Westerhuis, T. Kourti, and J. Macgregor, Analysis of multiblock and hierarchical PCA and PLS models, Journal of Chemometrics, vol.12, issue.5, pp.301-321, 1998.
DOI : 10.1002/(SICI)1099-128X(199809/10)12:5<301::AID-CEM515>3.0.CO;2-S

J. Westerhuis and A. Smilde, Deflation in multiblock PLS, Journal of Chemometrics, vol.156, issue.5, pp.485-493, 2001.
DOI : 10.1002/cem.652

S. Wold, N. Kettaneh, and K. Tjessem, Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection, Journal of Chemometrics, vol.10, issue.5-6, pp.463-482, 1996.
DOI : 10.1002/(SICI)1099-128X(199609)10:5/6<463::AID-CEM445>3.0.CO;2-L

S. Wold, M. Sjostrom, and L. Eriksson, Pls-regression : a basic tool of chemometrics . Chemometrics and intelligent laboratory systems, pp.109-130, 2001.

Z. Xiabo, M. J. Jiewen, M. Povey, M. Holmes, and . Hanpin, Variables selection methods in near-infrared spectroscopy, Analytica Chimica Acta, vol.667, issue.1-2, pp.14-32, 2010.
DOI : 10.1016/j.aca.2010.03.048

T. Yen, J. Erdman, and S. Pollack, Investigation of the Structure of Petroleum Asphaltenes by X-Ray Diffraction, Analytical Chemistry, vol.33, issue.11, pp.1587-1594, 1961.
DOI : 10.1021/ac60179a039

. En, initial ?x wlsb ) est négatif, le polynôme ajusté se trouve "au dessus" du spectre. Or, l'objectif est d'ajuster un polynôme sur la ligne de base. Un poids fort est alors attribué à ces points afin de "forcer" le polynôme à s'ajuster

L. Quelque-soit-la-méthode-appliquée and . Racine, carrée de la moyenne des erreurs quadratiques de validation croisée (RMSECV) est calculée pour chaque composante. Lors de la validation croisée, l'ajout de facteurs va diminuer la valeur de RMSECV jusqu'à une valeur minimale qui correspond

L. Wahl and . Duponchel, Characterisation of Heavy Oils using Near-Infrared Spectroscopy : Optimisation of Pre-processing Methods and Variable Selection, Analytica Chimica Acta, vol.705, issue.12, pp.227-234, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00627978

. Duponchel, Heavy Oil Characterisation by Vibrational Spectroscopy : Comparison of NIR and MIR ? Evaluation of Combination Methods, 15th International Conference on Near Infrared Spectroscopy ? Cape Town ? Afrique du Sud, 2011.

L. Wahl and . Duponchel, Characterisation of Heavy Oils using Near Infrared (NIR) Spectroscopy and Chemometrics, CAC Conference, Chemometrics in Analytical Chemistry) ? Anvers ? Belgique, 2010.