Pharmaceutical analysis by near infrared spectroscopy and chemometrics

OLIVIERI, A. C.

Buzios

Congreso; XXXVII Colloquium Spectroscopicum Internationale; 2011

CSI

Near infrared (NIR) spectroscopy is becoming a widespread tool for the analysis of diversematerials from a great variety of sources, such as food, petrochemistry products, biologicalsamples, pharmaceuticals, etc. Its main advantages are speed, automatization, field analysis,and most importantly, the possibility of non invasively studying intact materials. It showssome disadvantages, however, which are mainly tied to its lack of selectivity. However, thiscan be conveniently overcome by resorting to the available arsenal of chemometric dataprocessing techniques. They allow to mathematically extract the required informationconcerning analyte concentration or sample property from a set of partially selective NIRspectra. In the field of pharmaceutical analysis, this successful NIR/chemometrics combination is being increasingly applied for the control of active principles, raw material or quality of industrial end products, and for the detection of counterfeit drugs.1 A particularly growing field is the recording of NIR spectra in different points (pixels) of a pharmaceutical, e.g., a solid tablet, producing what is known as a hyperspectral data array. This is a threedimensional array whose dimensions are wavelength, x-position and y-position, where (x,y) defines the position of a given pixel. This information can be analyzed using a variety ofchemometric strategies, three of which will be discussed in this presentation: 1) partial leastsquares (PLS) regression, a multivariate calibration technique employed for predicting analyte concentrations or sample properties from an adequately calibrated model, 2) principalcomponent analysis (PCA), a powerful method which transforms the original data intoabstract spectra and scores, which can be employed for sample classification, and 3)multivariate curve resolution (MCR), a matrix data processing tool which decomposes thedata into the individual contributions (signal and position) of pure chemical components.2All of the above chemometrics method produce a map of the spatial distribution ofcomponents, which can be visually or statistically analyzed in order to characterize thesamples. Several examples will be discussed concerning the analysis of solid pharmaceuticalforms: 1) quality control of active principle and excipients, 2) presence of polymorphic forms, and 3) detection of counterfeit drugs.