A significance test for determining the optimum model dimension for partial
least squares regression is critically examined. It is derived that this s
ignificance test is equivalent with a well-known t-test for principal compo
nent regression. The results for a near-infrared data set suggest that the
conventional methods of optimising the prediction error estimate obtained f
rom a test set or cross-validation are to be preferred. (C) 2001 Elsevier S
cience B.V. All rights reserved.