Meningococcal polysaccharides identification by NIR spectroscopy and chemometrics.

Near-infrared (NIR) spectroscopy is an attractive tool for pharmaceutical analyses. The main purpose of this study was to assess the potential of NIR spectroscopy coupled with different multivariate classification tools for the identification of meningococcal polysaccharide serogroups A and C. Moreover, it sought to determine, if the models established on production batches, could be used to correctly identify National Institute for Biological Standards and Control standards. Two different classification tools were investigated: soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). Models' performance was evaluated by external validation. Although both models were able to correctly classify 100% of meningococcal polysaccharides from serogroups A and C, they performed differently in the presence of similar non-target serogroups W135 and Y. These results demonstrate that NIR spectroscopy, coupled with either SIMCA or PLS-DA, provides a method suitable for the identification of meningococcal polysaccharides A and C.