masterThesis
Cartas de controle multivariadas para o monitoramento simultâneo do teor de isoniazida e rifampicina em uma formulação farmacêutica empregando a espectroscopia no infravermelho próximo
Date
2014-10-27Registration in:
COSTA, Fernanda Saadna Lopes da. Cartas de controle multivariadas para o monitoramento simultâneo do teor de isoniazida e rifampicina em uma formulação farmacêutica empregando a espectroscopia no infravermelho próximo. 2014. 108f. Dissertação (Mestrado em Química) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2014.
Author
Costa, Fernanda Saadna Lopes da
Institutions
Abstract
Statistical process control is a strategy that has been increasingly used
for online monitoring of industrial processes. It is the main tool used for control
charts, which can be univariate or multivariate, according to the number of
variables involved in monitoring. Basically, control charts are statistical graphs
that have a tolerance range bounded by upper and lower lines, calculated
based on the standard deviation of the samples in control conditions. This paper
presents the application of multivariate control charts based on two
philosophies: the NAS (Net Analyte Signal) vector and the principal component
analysis (Principal Component Analysis - PCA), using Near Infrared
Spectroscopy for the simultaneous monitoring of level two drugs; isoniazid and
rifampicin in pharmaceutical formulations produced by NUPLAM (Center for
Research on Food and Drug Administration) at UFRN. The charts were
constructed using samples from an experimental design and the NUPLAM
production line. Control Charts via PCA scores for the simultaneous monitoring
of both APIs showed accuracy rates above 92% in the classification of samples
for calibration and validation, and 100% accuracy in prediction. The charts via
NAS to isoniazid had 100% accuracy in classifying samples of calibration and
prediction. The NAS charts to rifampicin achieved hit rates above 90.0% in the
calibration and prediction. The results obtained demonstrate the applicability of
multivariate control charts for the control of pharmaceutical processes, thereby
reducing cost and time of analysis, generating less waste and thus optimizing
the quality control process.