Estimating the influence of experimental parameters on the prediction error of PLS calibration models based on Raman spectra

Partial least squares (PLS) calibration is often the method of choice for making multivariate calibration models to predict analyte concentrations from Raman spectral measurements. In the development of such models, it is often difficult to assess beforehand what the prediction error will be, and whether instrumental or model factors limit the lower limit of the prediction error. Here, we present a method to assess the influence of experimental errors such as power fluctuations and spectral shifts, on the PLS prediction errors using simulated datasets. Assumptions that are implicit to PLS calibration and their implications with respect to the choice of experimental parameters for collecting a proper set of Raman spectra are discussed. The influence of various experimental parameters and signal pre-processing steps on PLS prediction error is demonstrated by means of simulations. The results of simulations are compared with the outcome of PLS calibrations of an experimental dataset. Copyright © 2006 John Wiley & Sons, Ltd.

[1]  T. Næs,et al.  Locally weighted regression and scatter correction for near-infrared reflectance data , 1990 .

[2]  P. Geladi,et al.  Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat , 1985 .

[3]  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 .

[4]  H. Martens,et al.  Extended multiplicative signal correction and spectral interference subtraction: new preprocessing methods for near infrared spectroscopy. , 1991, Journal of pharmaceutical and biomedical analysis.

[5]  Willem J. Melssen,et al.  Automatic correction of peak shifts in Raman spectra before PLS regression , 2000 .

[6]  On the Effect of Calibration and the Accuracy of NIR Spectroscopy with High Levels of Noise in the Reference Values , 1991 .

[7]  R. Sanderson,et al.  The Link between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra , 1994 .

[8]  K. Booksh,et al.  Influence of Wavelength-Shifted Calibration Spectra on Multivariate Calibration Models , 2004, Applied spectroscopy.

[9]  N. M. Faber,et al.  Uncertainty estimation for multivariate regression coefficients , 2002 .

[10]  Gerwin J. Puppels,et al.  A high‐throughput Raman notch filter set , 1990 .

[11]  Bruce R. Kowalski,et al.  Improved Prediction Error Estimates for Multivariate Calibration by Correcting for the Measurement Error in the Reference Values , 1997 .

[12]  Bruce R. Kowalski,et al.  Propagation of measurement errors for the validation of predictions obtained by principal component regression and partial least squares , 1997 .

[13]  S. D. Jong.  SIMPLS: an alternative approach to partial least squares regression , 1993 .

[14]  Age K Smilde,et al.  Performance optimization of spectroscopic process analyzers. , 2004, Analytical chemistry.

[15]  Desire L. Massart,et al.  Detection of nonlinearity in multivariate calibration , 1998 .

[16]  J. Coello,et al.  Effect of Day-To-Day Noise on UV-Visible Spectrophotometric Control Analyses of Mixtures by Principal Component Regression , 1996 .

[17]  Bruce R. Kowalski,et al.  PREDICTION ERROR IN LEAST SQUARES REGRESSION : FURTHER CRITIQUE ON THE DEVIATION USED IN THE UNSCRAMBLER , 1996 .

[18]  H. Martens,et al.  SHIFT AND INTENSITY MODELING IN SPECTROSCOPY-GENERAL CONCEPT AND APPLICATIONS , 1999 .

[19]  Desire L. Massart,et al.  Estimation of partial least squares regression prediction uncertainty when the reference values carry a sizeable measurement error , 2003 .

[20]  Richard G. Brereton,et al.  Introduction to multivariate calibration in analytical chemistry , 2000 .