Paracetamol polymorphs detection in suspension via a new ex situ Fourier Transform Near Infrared spectroscopy method
Résumé
This paper addresses the solid-state and in suspension characterization of the stable monoclinic form I and the metastable orthorhombic form II of paracetamol via differential scanning calorimetry (DSC), X-Ray diffraction (XRD) and Fourier transform near infrared spectroscopy (FT-NIR) methods. For generating the metastable form II, DSC technique was used and both polymorphs were characterized via XRD. It was demonstrated that form II crystals were stable until 29 days minimum of storage and no solid-state transformation occurred (form I trace less than 5 %). Additionally, the detection of this form in suspension was investigated via DSC, XRD and FT-NIR. The DSC was not a suitable method since it is highly sensitive to temperature and solution evaporation whereas XRD showed its capacity in detecting both forms in suspension but also in monitoring the solvent-mediated transformation for highly concentrated suspensions. On the other hand, ex situ FT-NIR method combined with partial least square discriminant analysis (PLS-DA) outperformed both methods. Indeed, it offered an outstanding distinction between paracetamol form I, form II and the mixture of the two forms, and an accurate monitoring of the solvent-mediated transformation. This approach showed good statistical performances with a low root mean square error of calibration (RMSEC), root mean square error of cross validation (RMSECV) and root mean square error of prediction (RMSEP) values (RMSEC=20 %; RMSECV=25 %; RMSEP=21 %), low classification errors (less than 3.2 %) and high specificity and sensitivity (higher than 93 %).
Mots clés
Paracetamol
Polymorphism
ex situ monitoring
Near InfraRed Spectroscopy
Principal component analyses PCA
PLS-DA
paracetamol polymorphism ex situ monitoring near infrared spectroscopy principal component analysis (PCA) partial least square discriminant analysis (PLS-DA)
paracetamol
polymorphism
near infrared spectroscopy
principal component analysis (PCA)
partial least square discriminant analysis (PLS-DA)
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