MULTIVARIATE DATA-ANALYSIS AS A TOOL FOR EVALUATING EMISSION INTENSITY, BACKGROUND EQUIVALENT CONCENTRATION AND DETECTION LIMIT OBTAINED FOR DIFFERENT PLASMA POSITIONS IN DIRECT-CURRENT PLASMA-ATOMIC EMISSION-SPECTROMETRY
R. Danielsson et al., MULTIVARIATE DATA-ANALYSIS AS A TOOL FOR EVALUATING EMISSION INTENSITY, BACKGROUND EQUIVALENT CONCENTRATION AND DETECTION LIMIT OBTAINED FOR DIFFERENT PLASMA POSITIONS IN DIRECT-CURRENT PLASMA-ATOMIC EMISSION-SPECTROMETRY, Analytica chimica acta, 354(1-3), 1997, pp. 211-224
Relative emission intensity, background emission concentration (BEC) a
nd detection limit (DL) obtained for different analytes and different
plasma positions are examples of multivariate data sets. The observati
ons can be related to the emission distribution in the plasma for the
different elements (the spatial profiles). Principal component analysi
s (PCA) as a tool for modelling, interpretation and visualisation of s
uch data sets was applied (i) to elucidate the data structure caused b
y the profiles, (ii) to enhance structural information using replicate
or similar data sets, (iii) to predict model results that are less pr
one to errors and random variations, and (iv) to compare data sets of
different origin (e.g. directly observed results with those calculated
from the profiles). The selection of a suitable optimisation element
can be guided by visual procedures or rather simple calculations based
on the PCA model. (C) 1997 Elsevier Science B.V.