« NIR
studies on cleaning verification of manufacturing equipment have also reported similar drying patterns for API soils [12] and dairy residues [4].
Linear Models Based on NIR Hyperspectral Datacubes for the Quantification of API Residue on Stainless Steel
Following the masking classification function, the resultant numbers of API classified pixels per applied soil concentration were fitted with a linear regression model. The obtained linear models for sulfadimidine sodium salt and sulfacetamide sodium salt are displayed in Figure 3. For sulfadimidine sodium salt and sulfacetamide sodium salt soils on stainless steel coupons the R2
values obtained were
0.96 and 0.99 respectively, which indicate a linear relationship between numbers of API classified pixels and applied API soil concentration. In Figure 3, it can also be observed that the number of API classified pixels varied noticeably between replicate soils of the same concentration, especially in the case of sulfadimidine sodium salt. The detected variability in replicate precision may be attributed to the drying of API water-based soils on stainless steel surfaces, as previously discussed which results in a non-homogenous soil with a ring-like appearance.
The limits of detection (LOD) for sulfadimidine sodium salt and sulfacetamide sodium salt soils on stainless steel coupons calculated according to the International Conference on Harmonisation (ICH) [11] were 27.10 and 13.68 µg / 50 mm2
respectively.
However, visual examination of the images presented in Figure 2 indicate that there is potential to lower the limit of detection
. The calculated limits of detection reflect the residual standard error (RSE) of the linear model fitted to the experimental data (number of API classified pixels versus applied soil concentration). The RSE is in turn affected by the variability in the number of pixels classified as API between replicate API residues at the same concentration level. The use of different protocols to simulate the soiling process in industrial manufacturing equipment may result in improved soil uniformity and improved replicate precision, contributing to lowering of the LOD in cleaning verification by NIR-CI.
of API residues to values circa 1 µg 50 mm- 2
In addition, further analysis of the NIR hyperspectral datacubes may be conducted
www.americanpharmaceuticalreview.com | | 107
An introduction on the use of FT-NIR for raw material and finished product identification in the Nutraceutical industry will be presented. A widely used technique in the pharmaceutical industry for ingredient identification, FT-NIR is a fast and nondestructive technique that provides chemical and physical properties on virtually any matrix.
Presenters:
Gayle Kittelson Quality Manager, Embria Health Sciences
Mark Terrell FT-NIR Product Manager, BUCHI Corporation
Raw Material Analysis by Fourier Transform Near Infrared (FT-NIR) Spectroscopy
to develop robust and accurate models with possibly lower limits of detection for the quantification of API residues. In the present feasibility study, the quantification of API soils was based on regression models developed using solely the number of API pixels identified by the classification function. More elaborated classification functions may be developed and tested, as well as algorithms to extract information related to soil thickness that may result in an improved quantification of API soils in cleaning verification using NIR-CI.
Join American Pharmaceutical Review and BUCHI Corporation for this on-demand webinar
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http://goo.gl/Yc3p9B
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