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non-relevant spectral variation and improve model statistics. As a pre-
processing method, Savitzky-Golay fi rst derivative with a second-order
polynomial fi t using 17 spectral points was selected as the optimal one.
The fi rst 3 LVs explained 99.08% of the variation in
X
and 98.70% of
Y
variation. With 3 LVs, the RMSEC was 0.37 and the RMSECV was 0.53.
This example demonstrated how a regression model between in-line NIR
spectra and LOD provides monitoring capability for the fl uid-bed drying
process. It is possible to implement the model for real-time control of the
drying. Another regression model was developed in the study to correlate
process variables and fi nal quality characteristic of the product - mean
disintegration time for the tablets.
Two PLS models were developed (models I and II) using process
variables and NIR spectra as predictors of tablet disintegration time. The
NIR spectra consisted of more than 2250 spectral variables and, in order
to perform data fusion between a few process variables and thousands of
spectral variables, the NIR spectra were fi rst decomposed using PCA and
the mean centered scores were then fused with the process variables.
Then the scores and process variables were auto-scaled and a PLS model
established between the predictors and the mean disintegration time.
Process variables used as predictors were mixing time and granulation
liquid fl ow (model I), and drying temperature, drying time, and upper
punch force during tableting (model II). Model I used the fi rst three PCs
of the PCA model of average NIR spectrum from the mixing, whereas
model II used the fi rst three scores from three PCA models of the average
NIR spectrum of granulation, end of drying process, and glidant mixing
step. The root mean squared error obtained from cross-validation
(RMSECV) of model II was 35.0 with 1 LV and 85.4% of the Y variation
explained compared to 61.5% for model I. Thus, by adding more process
information, the prediction error decreased and a better model was
established. The prediction error of model II was also close to the
standard deviation for the reference analysis (~30 s), so it might be
diffi cult to improve the model further using the existing data. Both
models I and II can be used for process control, for example, when
adjusting granulation liquid fl ow (model I) or upper punch force during
tableting (model II), since their infl uence on the tablet disintegration time
is deciphered. Of course, defi nition of optimal processing parameters
(and their changing) can never be dependent on just one quality attribute
of a product (fi nal or intermediate).
NIR was used for the quantifi cation of API and excipients of a
pharmaceutical formulation, accompanied by PCA and PLS analysis
(Sarraguça and Lopes, 2009). The developed method was based on
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