Chemometric methods application in pharmaceutical products and processes analysis and control - Computer-Aided Applications in Pharmaceutical Technology - page 83

black for the particle size μm] classes and red for the other
response variables) for the fi rst two principal components (58
and 22% of the total variance in the data set is captured by
PCs 1 and 2, respectively). Independent (predictor) variables in
this example are particle size density distributions. For scores
and loadings to be displayed on one plot, scaling of values is
necessary (−1 to +1). Potency, homogeneity (%RSD), and
effective dose (ED) are poorly modeled (since they are close to
the center of the coordinate system), suggesting there is less
variability in these results and that the variability of the other
factors included dominate the PCA. PC1 captures the particle
size variation in terms of fi ne and coarse particles - the fi ne
particle size classes are on the right side of the plot and the
coarse are to the left side. Therefore, blends 6 and 9 on the
right will have a larger coarse component and blends 3 and 4
on the left will have a larger fi ne component. The fi ne particle
size classes (<10 μm) are seen to positively correlate with
the FPD and Carr's Index and negatively with the fl ow function
coeffi cient (FFC). FPD values obtained on stability studies are
correlated with initial FPDs. Second principal component, PC2,
does not provide as much information, since it is covered by mid-
range particle sizes. Findings are in agreement with the DoE
results.

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Example 3

Microwave resonance technology (MRT) was used for in-line
monitoring of fl uid-bed granulation process (Lourenço et al., 2011).
MRT was used to determine moisture, temperature, and density of
the granules. PLS, PCA, and multivariate batch control were used,
applied on collected batch data sets. Particle size measurements
were performed off-line. The PLS method demonstrated the ability
to defi ne a quantitative relationship between MRT measurements
and particle size, therefore enabling use of MRT for process
monitoring and prediction of fi nal particle size. This is an example