Quality control of the powder pharmaceutical samples of sulfaguanidine by using NIR reflectance spectrometry and temperature-constrained cascade correlation networks

Temperature-constrained cascade correlation networks (TCCCNs) were applied to the identification of the powder pharmaceutical samples of sulfaguanidine based on near infrared (NIR) diffuse reflectance spectra and their first derivative spectra. This work focused on the comparison of performances of the uni-output TCCCN (Uni-TCCCN) and multi-output (Multi-TCCCN) by near infrared diffuse reflectance spectra and their first derivative spectra of sulfaguanidine. The TCCCN models were verified with independent prediction samples by using the “cross-validation” method. The networks were used to discriminate qualified, un-qualified and counterfeit sulfaguanidines pharmaceutical powders. The results showed that single outputs network generally worked better than the multiple outputs networks, and the first derivative spectra were more suitable for the identification comparing with original diffuse reflectance spectra. With proper network parameters the pharmaceutical powders can be classified at rate of 100% in this work. Also, the effects of parameters and related problems were discussed.