Input variable scaling for statistical modeling

Barnes, 1989, Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra, Appl Spectrosc, 43, 772, 10.1366/0003702894202201 van den Berg, 2006, Centering, scaling, and transformations improving the biological information content of metabolomics data, BMC Genomics, 7, 142, 10.1186/1471-2164-7-142 Cen, 2007, Theory and application of near infrared reflectance spectroscopy in determination of food quality, Trends Food Sci Technol, 18, 72, 10.1016/j.tifs.2006.09.003 Engel, 2013, Breaking with trends in pre-processing?, Trends Anal Chem, 50, 96, 10.1016/j.trac.2013.04.015 Fujiwara, 2012, Input variable selection for PLS modeling using nearest correlation spectral clustering, Chemom Intell Lab Syst, 118, 109, 10.1016/j.chemolab.2012.08.007 Hocking, 1976, A biometrics invited paper. The analysis and selection of variables in linear regression, Biometrics, 32, 1, 10.2307/2529336 Jamrógiewicz, 2012, Application of the near-infrared spectroscopy in the pharmaceutical technology, J Pharm Biomed, 66, 1, 10.1016/j.jpba.2012.03.009 Jiang, 2002, Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data, Anal Chem, 74, 3555, 10.1021/ac011177u Jouen-Rimbauda, 1995, Genetic algorithms as a tool for wavelength selection in multivariate calibration, Anal Chem, 67, 4295, 10.1021/ac00119a015 Kadlec, 2009, Data-driven soft sensors in the process industry, Comput Chem Eng, 33, 795, 10.1016/j.compchemeng.2008.12.012 Kano, 2013, Virtual sensing technology in process industries: trends and challenges revealed by recent industrial applications, J Chem Eng Jpn, 46, 1, 10.1252/jcej.12we167 Keun, 2003, Improved analysis of multivariate data by variable stability scaling application to NMR-based metabolic profiling, Anal Chim Acta, 490, 265, 10.1016/S0003-2670(03)00094-1 Khatibisepehr, 2014, A probabilistic framework for real-time performance assessment of inferential sensors, Control Eng Pract, 26, 136, 10.1016/j.conengprac.2014.01.019 Kim, 2013, Long-term industrial applications of inferential control based on just-in-time soft-sensors: Economical impact and challenges, Ind Eng Chem Res, 52, 12346, 10.1021/ie303488m Kim, 2011, Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection, Int J Pharm, 421, 269, 10.1016/j.ijpharm.2011.10.007 Kuzmanovski, 2009, Automatic adjustment of the relative importance of different input variables for optimization of counter-propagation artificial neural networks, Anal Chim Acta, 642, 142, 10.1016/j.aca.2009.01.041 Martens, 2003, Pre-whitening of data by covariance-weighted pre-processing, J Chemom, 17, 153, 10.1002/cem.780 Nakagawa, 2012, Evaluation of infrared-reflection absorption spectroscopy measurement and locally weighted partial least-squares for rapid analysis of residual drug substances in cleaning processes, Anal Chem, 84, 3820, 10.1021/ac202443a Nørgaard, 2000, Interval partial least-squares regression (iPLS): A comparative chemometric study with an example from near-infrared spectroscopy, Appl Spectrosc, 54, 413, 10.1366/0003702001949500 Oh, 2013, Real-time estimation of glucose concentration in algae cultivation system using Raman spectroscopy, Bioresour Technol, 142, 131, 10.1016/j.biortech.2013.05.008 Rajalahti, 2011, Multivariate data analysis in pharmaceutics: a tutorial review, Int J Pharm, 417, 280, 10.1016/j.ijpharm.2011.02.019 Roggo, 2007, A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies, J Pharm Biomed Anal, 44, 683, 10.1016/j.jpba.2007.03.023 Savitzky, 1964, Smoothing and differentiation of data by simplified least squares procedures, Anal Chem, 36, 1627, 10.1021/ac60214a047 Tibshirani, 1996, Regression shrinkage and selection via the lasso, J R Statist Soc Ser B, 58, 267 Todeschini, 1999, The k correlation index theory development and its application in chemometrics, Chemom Intell Lab Syst, 46, 13, 10.1016/S0169-7439(98)00124-5 Wold, 2001, PLS-regression: a basic tool of chemometrics, Chemom Intell Lab Syst, 58, 109, 10.1016/S0169-7439(01)00155-1