Multivariate analysis and classification of the chemical quality of 7-aminocephalosporanic acid using near-infrared reflectance spectroscopy

The capability of near-infrared (NIR) spectroscopy in comparison to conventional chemical testing to control the chemical quality of a pharmaceutical intermediate has been investigated. Multivariate projection methods including principal component analysis, partial least-squares discriminant analysis and soft independent modeling of class analogy have been evaluated. 7-Amino-cephalosporanic acid has been chosen as an example providing a large variation of quality due to its relative chemical instability. Three sets of production lots have been selected to study the extent of quality information extractable from NIR spectra. The first set of 91 lots covers a very broad range of chemical quality assessed by 8 parameters with a partially extented characterization by physical properties. The general congruence of spectral, chemical, and physical information has been investigated. The second set of 110 lots covers a very narrow range of chemical quality assessed by 11 parameters. With extended quality information, the intrinsic selectivity within the spectral data structure has been studied. The third set of 228 lots characterized by 8 parameters is a selection out of more than 1000 lots over a production period of two years. The ruggedness of the multivariate approach has been confirmed by a cross validation of the classification test.