Development of Robust Multivariate Calibration Models

Multivariate calibration is frequently used for the quantitative analysis of a wide variety of materials using spectroscopy in the agricultural and food industries, manufacturing industries, medical sciences, and pharmaceutical industries. To date, most of the research activities in the multivariate calibration literature have focused on data analysis for model building and prediction with methods such as principal-components regression or partial least squares regression. As an alternative to focusing on data-analytic activities, we consider the ability of an experimental design to improve the robustness of the resulting calibration model. Through an example involving diffuse reflectance measurements, we illustrate how consideration of environmental and instrumental factors during the experimental design phase can result in a calibration model that is robust (against natural environmental and instrument variations) and easy to maintain. In this example, the analyte of interest produces a spectroscopic signal that is very weak in comparison to the environmental and instrumental factors.

[1]  J. Friedman,et al.  A Statistical View of Some Chemometrics Regression Tools , 1993 .

[2]  E. V. Thomas,et al.  COMPARISON OF MULTIVARIATE CALIBRATION METHODS FOR QUANTITATIVE SPECTRAL ANALYSIS , 1990 .

[3]  D. Hawkins,et al.  Analysis of a Large Structure‐Activity Data Set Using Recursive Partitioning , 1997 .

[4]  David M. Haaland,et al.  Infrared spectroscopic techniques for quantitative characterization of dielectric thin films on silicon wafers , 1994 .

[5]  S. Oman,et al.  Estimating fetal age by ultrasound measurements: an example of multivariate calibration. , 1984, Biometrics.

[6]  Arden Miller,et al.  Parameter Design for Signal-Response Systems: A Different Look at Taguchi's Dynamic Parameter Design , 1996 .

[7]  Madhan Shridhar Phadke,et al.  Quality Engineering Using Robust Design , 1989 .

[8]  G. Geoffrey Vining,et al.  Taguchi's parameter design: a panel discussion , 1992 .

[9]  Edward V. Thomas,et al.  Errors-in-variables estimation in multivariate calibration , 1991 .

[10]  H. Martens,et al.  Comparison of Linear Statistical Methods for Calibration of Nir Instruments , 1986 .

[11]  Tormod Næs,et al.  Multivariate Calibration When the Error Covariance Matrix Is Structured , 1985 .

[12]  H M Heise.  Non-invasive monitoring of metabolites using near infrared spectroscopy: state of the art. , 1996, Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme.

[13]  D. T. Witte,et al.  Vibrational spectrometry for the assessment of active substance in metoprolol tablets: a comparison between transmission and diffuse reflectance near-infrared spectrometry. , 1996, Journal of pharmaceutical and biomedical analysis.

[14]  M Defernez,et al.  Infrared spectroscopy:  instrumental factors affecting the long-term validity of chemometric models. , 1997, Analytical chemistry.

[15]  L. Gleser.  Measurement, Regression, and Calibration , 1996 .

[16]  Bruce R. Kowalski,et al.  Calibration Transfer and Measurement Stability of Near-Infrared Spectrometers , 1992 .

[17]  Richard L. Smith,et al.  Measuring Marathon Courses: An Application of Statistical Calibration Theory , 1987 .

[18]  D. Massart,et al.  Standardisation of near-infrared spectrometric instruments: A review , 1996 .

[19]  James Durbin,et al.  Errors in variables , 1954 .

[20]  Philip J. Brown,et al.  Wavelength selection in multicomponent near‐infrared calibration , 1992 .

[21]  Sidney Addelman,et al.  trans-Dimethanolbis(1,1,1-trifluoro-5,5-dimethylhexane-2,4-dionato)zinc(II) , 2008, Acta crystallographica. Section E, Structure reports online.

[22]  R. N. Kackar.  Off-Line Quality Control, Parameter Design, and the Taguchi Method , 1985 .