This study details the analysis of thrombin, a lyophilized topical coagulant, by FT-NIR spectroscopy. Two critical parameters
for thrombin are moisture (usually below 1.0%) and potency. Traditionally, moisture measurements are carried out by Karl Fischer
titration, a USP-referenced technique (USP <921>). Potency is established using light- scattering measurements after the sample is titrated with plasma. If the sample
potency is high, the thrombin will coagulate the plasma, creating particulates. Greater potency creates more particulates,
resulting in increased light scattering from the sample. Both of these techniques are destructive and require a skilled operator.
Both are also time-consuming and use consumables like solvents, plasma, or reagents.
Figure 3. The Percent Residual for Moisture.
Two sets of ten thrombin samples were used to establish calibrations for moisture and potency. The samples were pulled from
finished product lots and analyzed through the vial using a Nicolet Antaris FT-NIR analyzer with a LyoCheck solution package
from Thermo Electron Corp. The autosampler RS attachment was used to collect all sample data without operator interaction.
Thirty-two scans were taken of each sample and co-averaged at a resolution of 4.0 cm–1 . Each sample took approximately 20 sec to analyze. The wavelength range scanned was 4,000 cm–1 to 10,000 cm–1 . Once the samples were analyzed on the FT-NIR spectrometer, they were analyzed destructively using the primary technique
— Karl Fischer titration for the ten moisture samples and light-scattering plasma titration for potency. These primary numbers
were then combined with the spectral data in multivariate analysis methods using TQ Analyst, a chemometric analysis software
Figure 4. The Cross Validation Curve for Moisture.
Spectral data from the FT-NIR analyzer were combined with primary numbers from Karl Fischer titration for ten production samples.
The moisture values ranged approximately from 0.5% to 0.8%. The data were combined using the analytic software to construct
a chemometric model using the Stepwise Multiple Linear Regression (SMLR) algorithm. Data pretreatment of NIR spectra is common
to help the various algorithms predict analytes accurately. In some cases, the primary or secondary derivative of the raw
spectrum may prove more effective for measurement. Other pretreatments include pathlength algorithms like multiplicative scatter
correction or standard normal variate, smoothing, or baseline corrections. In this case, the pretreatments were minimal as
moisture is usually a strong absorber in the NIR range. The second derivative of the spectra was used for making the calibration
curve with a 9,2 Norris smoothing filter. The regions chosen were constrained around 7,000 cm-1 , the first water overtone band. The water overtone region for the ten standards is shown in Figure 1.