Abstract
The effect of data pre-processing (no pre-processing, offset correction, de-trending, standard normal variate transformation (SNV), SNV+de-trending, multiplicative scatter correction, first and second derivative transformation after smoothing) on the identification of ten pharmaceutical excipients is investigated. Four pattern recognition methods are tested in the study, namely the Mahalanobis distance method, the SIMCA residual variance method, the wavelength distance method and a method based on triangular potential functions. The performance of the 32 method combinations is evaluated on the basis of two NIR data sets. The first one, measured in 1994, is used to build the classification models, the second, measured from 1994–1997, is used to assess the quality of the models. The best approach for the given data sets is the wavelength distance method combined with de-trending, a simple baseline correction method. More general recommendations for pre-processing excipient NIR data and for choosing an appropriate classification method are given.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
