This paper is concerned with the possibility of obtaining chemically meanin
gful models of complicated processes by the use of fluorescence spectroscop
y screening and the unique parallel factor analysis (PARAFAC) model. The se
cond-order nature of fluorescence excitation emission data and the fact tha
t the PARAFAC model has no rotational indeterminacy mean that in certain ca
ses, it is possible to decompose complex mixture signals into contributions
from individual chemical components. Relating the thus obtained informatio
n to, e.g., important quality parameters, it is possible to analyze, unders
tand, predict and monitor the quality based on a chemical foundation. The p
roposed approach thus gives a direct link between process analytical chemis
try and multivariate statistical process control. (C) 1999 Elsevier Science
B.V. All rights reserved.