The different physical characteristics and treatments of the solid samples are responsible for the spectral variability that takes place in near infrared (NIR) measures. These changes, not related to the analyte concentration, may yield in complex and not very robust calibration models. Mathematical treatments are usually applied for the correction of this variability, being the most common derivation, standard normal variate (SNV) and multiplicative scatter correction (MSC). Orthogonal signal correction (OSC) is a new mathematical treatment designed to minimize, in a set of spectral data, the variability not related with the concentration of the analyte. In this work the application of this new treatment to minimize the spectral differences of two types of samples: production samples and laboratory samples, is evaluated. A method is developed for the determination of the content of the active component in a pharmaceutical preparation by means of PLS calibration. Results obtained by OSC are compared with those obtained with the original data and with those corrected by derivation, SNV and MSC. OSC treatment leads to PLS calibration models with good prediction ability and simpler than those obtained using other pretreatments. © 2001 Elsevier Science B.V.
|Journal||Analytica Chimica Acta|
|Publication status||Published - 25 Apr 2001|
- Data pretreatment
- Near infrared spectroscopy
- Orthogonal signal correction
- Pharmaceutical analysis