Quantitative on-line vs. off-line NIR analysis of fluidized bed drying with consideration of the spectral background

Quantitative dehydration studies of dibasic calcium phosphate anhydrous (DCPA) in a small-scale cold-model fluidized bed dryer with process air control were conducted. Near-infrared spectroscopy (NIRS) with partial least squares regression (PLSR) was used to predict DCPAs’ residual moisture content....

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Veröffentlicht in:European journal of pharmaceutics and biopharmaceutics 2013-11, Vol.85 (3), p.1064-1074
Hauptverfasser: Heigl, Nicolas, Koller, Daniel M., Glasser, Benjamin J., Muzzio, Fernando J., Khinast, Johannes G.
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Sprache:eng
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Zusammenfassung:Quantitative dehydration studies of dibasic calcium phosphate anhydrous (DCPA) in a small-scale cold-model fluidized bed dryer with process air control were conducted. Near-infrared spectroscopy (NIRS) with partial least squares regression (PLSR) was used to predict DCPAs’ residual moisture content. Loss-on-drying (LOD) was employed as a reference method and confirmed the actual moisture content of DCPA. First, dynamic PLSR modeling was carried out, i.e., the NIR spectra were on-line recorded and predicted throughout the drying process. Secondly, PLSR off-line modeling was performed, i.e., samples were consecutively thief-probed from the processor, put into glass vials and analyzed off-line. Furthermore, two background spectra were collected prior to the in- and off-line measurements in an attempt to increase the method’s sensitivity, i.e., (i) dry DCPA that was fluidized at respective process air velocity (on-line) or inside a glass vial (off-line) and (ii) Spectralon® – a highly reflecting standard reference material made of fluoropolymer. Benefits and drawbacks of the in- and off-line approaches with different spectral backgrounds are discussed in detail. The results indicated that (i) the thief-probed sample amount from the processor and thus the sample weight and (ii) the downtime between thief-probing a sample and its actual analysis via NIRS and LOD can bias the moisture content predictions.
ISSN:0939-6411
1873-3441
DOI:10.1016/j.ejpb.2013.09.012