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key wavelengths and showed that the excipients were responsible for the
differences (Roggo et al., 2004). Similarly, different sites of production of
various proprietary tablets were compared. The PCA score plots showed
that NIR spectra of tablets originating from different sites of manufacture
often gave rise to statistically different populations. PCA loadings
indicated that the differences were related to moisture content and
excipients (Yoon et al., 2004).
NIRS was used to detect and identify changes in uncoated and coated
tablets in response to pilot-scale changes in process parameters during
melt granulation, compression, and coating (Roggo et al., 2005). It was
shown that NIRS and PCA were capable of separating batches produced
with different melt granulation parameters and could differentiate
between cores compressed with different compression forces. PLS
regression was used to predict production sample coating times and
dissolution rates from the NIRS data.
The accuracy of linear and quadratic discriminant analysis (LDA and
QDA) and the KNN method has been evaluated on tablet and capsule
data sets, to classify samples for clinical studies (Candolfi et al., 1998).
SIMCA was applied to identify NIR spectra of 10 pharmaceutical
excipients (Candolfi et al., 1999). Also, it was used for NIRS identifi cation
of counterfeit drugs (Scafi and Pasquini, 2001).
The cascade correlation neural (CCN) network was used to classify
qualifi ed, unqualifi ed, and counterfeit sulfaguanidine pharmaceutical
powders (Cui et al., 2004).
PCA was applied to pharmaceutical powder compression (Roopwani
and Buckner, 2011). A solid fraction parameter and a mechanical work
parameter representing irreversible compression behavior were
determined as functions of the applied load. The fi rst principal component
(PC1) showed loadings for the solid fraction and work values that agreed
with changes in the relative signifi cance of plastic deformation to
consolidation at different pressures. The utility of PC1 in understanding
deformation was extended to binary mixtures using a subset of the
original materials.
Raman spectroscopy was used for identifi cation of tablets (Roggo
et al., 2010). Twenty-fi ve product families of tablets have been included
in the spectral library and a non-linear classifi cation method, the SVM,
was employed. Two calibrations were developed in the cascade; the fi rst
identifi es the product family, while the second specifi es the formulation.
A product family comprises different formulations that have the same
active pharmaceutical ingredient (API) but in a different amount. The
correlation with the reference spectra and the control of the API peak
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