Recognition of active ingredients in tablets by chemometric processing of X-ray diffractometric data.
The paper presents an approach to use Partial Least Squares Discriminant Analysis (PLS-DA) on X-ray powder diffractometry (XRPD) dataset to build a model which recognizes a presence (or absence) of particular drug substance (acetaminophen) in unknown mixture (OTC tablet). The dataset consisted of 33 XRPD signals, measured for 12 pure substances and 21 tablets containing them in different quantitative and qualitative ratios, along with unknown excipients. The model was built with an external validation dataset chosen by Kennard-Stone algorithm. The RMSECV value was equal to 0.3461 (87.8% of explained variance) and external predictive error (RMSEP) was equal to 0.3123 (86.2% of explained variance). The result suggests that small but properly prepared training datasets give ability to construct well-working discriminant models on XRPD signals.
Komsta
Łukasz
Ł
Department of Medicinal Chemistry, Medical University of Lublin, Lublin, Poland. lukasz.komsta@umlub.pl
Maurin
Jan K
JK
D000082:Acetaminophen / Q000032:analysis; D018712:Analgesics, Non-Narcotic / Q000032:analysis; D008956:Models, Chemical; D011208:Powders / Q000737:chemistry; D013607:Tablets / Q000737:chemistry*; D014961:X-Ray Diffraction / Q000379:methods
D018712:Analgesics, Non-Narcotic; D011208:Powders; D013607:Tablets; D000082:Acetaminophen