M. Blanco et al., Calibration in non-linear near infrared reflectance spectroscopy: a comparison of several methods, ANALYT CHIM, 384(2), 1999, pp. 207-214
Principal component regression (PCR) and partial least-squares regression (
PLSR) are the two calibration procedures most frequently used in quantitati
ve applications of near infrared diffuse reflectance spectroscopy (NIRRS).
Some systems, however, exhibit a non-linear relationship that neither metho
dology can model. Frequently, the main culprit of such nonlinearity is the
multiplicative effect arising from non-uniform particle sizes or diameters
in the samples.
In this work, we tested various approaches to minimizing the non-linearity
resulting from the multiplicative effect of differences in particle size or
sample thickness, using the determination of linear density in acrylic fib
res as physical model. The approaches tested involve the prior Linearizing
of data by logarithmic conversion and/or the use of non-linear calibration
systems; in this context, the results of applying stepwise polynomial PCR (
SWP-PCR) and PLSR (SWP-PLSR), and those provided by a neural network based
on the scores of the PCR model (PC-ANN), were compared.
The PC-ANN approach was found to provide the best results with linear densi
ty data. On the other hand, the SWP-PLSR approach performed on par with the
previous one when the variable was linearized by conversion of its values
into decimal logarithms. (C) 1999 Elsevier Science B.V. All rights reserved
.