title: Detection of drug active ingredients by chemometric processing of solid-state NMR spectrometry data -- the case of acetaminophen.
abstract: This paper presents a preliminary study in building discriminant models from solid-state NMR spectrometry data to detect the presence of acetaminophen in over-the-counter pharmaceutical formulations. The dataset, containing 11 spectra of pure substances and 21 spectra of various formulations, was processed by partial least squares discriminant analysis (PLS-DA). The model found coped with the discrimination, and its quality parameters were acceptable. It was found that standard normal variate preprocessing had almost no influence on unsupervised investigation of the dataset. The influence of variable selection with the uninformative variable elimination by PLS method was studied, reducing the dataset from 7601 variables to around 300 informative variables, but not improving the model performance. The results showed the possibility to construct well-working PLS-DA models from such small datasets without a full experimental design.
authors: Medical University of Warsaw, Department of Physical Chemistry, Warsaw, Poland. kparad75@gmail.com
authors: Katarzyna
authors: K
authors: Paradowska
authors: Marta Katarzyna
authors: MK
authors: Jamróz
authors: Mariola
authors: M
authors: Kobyłka
authors: Ewelina
authors: E
authors: Gowin
authors: Paulina
authors: P
authors: Maczka
authors: Robert
authors: R
authors: Skibiński
authors: Łukasz
authors: Ł
authors: Komsta
mesh_terms: D000082:Acetaminophen / Q000032:analysis*; D016018:Least-Squares Analysis; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D025341:Principal Component Analysis
chemical_list: D000082:Acetaminophen