Assessment of the Quality of Latent Variable Calibrations Based on Monte Carlo Simulations

A general problem in multivariate calibration is the assessment of the prediction quality in advance, i.e., without extensive experimentation. Monte Carlo simulations are proposed to intimate the quality of latent variable calibrations and thereby to minimize experimental work and to save resources. Two different data sets from industrial practice illustrate that tits approach can be applied successfully for very different problems. The first set consists of 100 samples of penicillin analyzed by near-IR spectroscopy. The second data set contains visible spectra of 37 rumples of a dyestuff intermediate. Based on the knowledge of the noise in the calibration method, the noise in the reference method the spectra of the analytes, the concentration range used for calibration, and the number of calibration samples, the prediction quality can be estimated

[1]  Paul J. Gemperline,et al.  Nonlinear multivariate calibration using principal components regression and artificial neural networks , 1991 .

[2]  A. Höskuldsson.  PLS regression methods , 1988 .

[3]  Paul J. Gemperline,et al.  Mixture analysis using factor analysis I: Calibration and quantitation , 1989 .

[4]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[5]  J. Blackburn.  Computer Program for Multicomponent Spectrum Analysis Using Least Squares Method. , 1965 .

[6]  H. R. Keller,et al.  Peak purity control in liquid chromatography with photodiode-array detection by a fixed size moving window evolving factor analysis , 1991 .

[7]  Bruce R. Kowalski,et al.  Recent developments in multivariate calibration , 1991 .

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

[9]  Relationship between digital filtering and multivariate regression in quantitative analysis , 1992 .

[10]  Bruce R. Kowalski,et al.  The effect of mean centering on prediction in multivariate calibration , 1992 .

[11]  Chris W. Brown,et al.  Matrix representations and criteria for selecting analytical wavelengths for multicomponent spectroscopic analysis , 1982 .

[12]  S. Wold.  Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[13]  H. Kisner,et al.  Multiple analytical frequencies and standards for the least-squares spectrometric analysis of serum lipids. , 1983, Analytical chemistry.

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

[15]  Gerrit Kateman,et al.  Realistic simulations of high-performance liquid chromatographic—ultraviolet data for the evaluation of multivariate techniques , 1991 .

[16]  D. Haaland,et al.  Multivariate Least-Squares Methods Applied to the Quantitative Spectral Analysis of Multicomponent Samples , 1985 .

[17]  E. V. Thomas,et al.  Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information , 1988 .