Online updating of NIR model and its industrial application via adaptive wavelength selection and local regression strategy
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[1] Biao Huang,et al. Recursive Wavelength-Selection Strategy to Update Near-Infrared Spectroscopy Model with an Industrial Application , 2013 .
[2] Roman M. Balabin,et al. Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction , 2007 .
[3] Hiromasa Kaneko,et al. A new process variable and dynamics selection method based on a genetic algorithm‐based wavelength selection method , 2012 .
[4] A Garrido-Varo,et al. Non-linear regression methods in NIRS quantitative analysis. , 2007, Talanta.
[5] En Sup Yoon,et al. Weighted support vector machine for quality estimation in the polymerization process , 2005 .
[6] Xueguang Shao,et al. Application of latent projective graph in variable selection for near infrared spectral analysis , 2012 .
[7] Roman M. Balabin,et al. Gasoline classification by source and type based on near infrared (NIR) spectroscopy data , 2008 .
[8] Manabu Kano,et al. Soft‐sensor development using correlation‐based just‐in‐time modeling , 2009 .
[9] Zhi-huan Song,et al. Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes , 2011 .
[10] Zou Xiaobo,et al. Variables selection methods in near-infrared spectroscopy. , 2010, Analytica chimica acta.
[11] M. Chiu,et al. A new data-based methodology for nonlinear process modeling , 2004 .
[12] C. Jun,et al. Performance of some variable selection methods when multicollinearity is present , 2005 .
[13] Roman M. Balabin,et al. Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data. , 2011, Analytica chimica acta.
[14] Tahir Mehmood,et al. A review of variable selection methods in Partial Least Squares Regression , 2012 .
[15] Manabu Kano,et al. Development of soft-sensor using locally weighted PLS with adaptive similarity measure , 2013 .
[16] Rasmus Bro,et al. Variable selection in regression—a tutorial , 2010 .
[17] W. Cai,et al. A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra , 2008 .
[18] Romà Tauler,et al. Application of the local regression method interval partial least-squares to the elucidation of protein secondary structure. , 2005, Analytical biochemistry.
[19] Kimito Funatsu,et al. Genetic algorithm‐based wavelength selection method for spectral calibration , 2011 .
[20] Celio Pasquini,et al. A PLS regression model using NIR spectroscopy for on-line monitoring of the biodiesel production reaction , 2011 .
[21] Manabu Kano,et al. Long-Term Industrial Applications of Inferential Control Based on Just-In-Time Soft-Sensors: Economical Impact and Challenges , 2013 .
[22] Hongdong Li,et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. , 2009, Analytica chimica acta.
[23] Jordi Coello,et al. Calibration in non-linear near infrared reflectance spectroscopy: a comparison of several methods , 1999 .
[24] Zhi-huan Song,et al. Online monitoring of nonlinear multiple mode processes based on adaptive local model approach , 2008 .
[25] Fei Liu,et al. Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar. , 2009 .
[26] Jordi Coello,et al. NIR calibration in non-linear systems: different PLS approaches and artificial neural networks , 2000 .
[27] Manabu Kano,et al. Optimum quality design system for steel products through locally weighted regression model , 2011 .
[28] Roman M. Balabin,et al. Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy , 2011 .
[29] Charles R. Hurburgh,et al. A Tutorial on Near Infrared Spectroscopy and Its Calibration , 2010 .
[30] Christian R. Mora,et al. On the selection of samples for multivariate regression analysis : application to near-infrared (NIR) calibration models for the prediction of pulp yield in Eucalyptus nitens , 2008 .
[31] I. Karimi,et al. Scheduling Gasoline Blending Operations from Recipe Determination to Shipping Using Unit Slots , 2011 .
[32] T. Isaksson,et al. New approach for distance measurement in locally weighted regression , 1994 .
[33] Roman M. Balabin,et al. Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. , 2011, The Analyst.
[34] Y. Roggo,et al. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. , 2007, Journal of pharmaceutical and biomedical analysis.
[35] Manabu Kano,et al. Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection. , 2011, International journal of pharmaceutics.
[36] J. Roger,et al. Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes , 2004 .
[37] Quansheng Chen,et al. Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms. , 2008, Journal of pharmaceutical and biomedical analysis.
[38] M A Arnold,et al. Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy. , 1996, Analytical chemistry.
[39] S. Wold,et al. Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data. , 2002, Analytical chemistry.
[40] Ning Wang,et al. A bio-inspired algorithm based on membrane computing and its application to gasoline blending scheduling , 2011, Comput. Chem. Eng..