摘要
Temperature-constrained cascade correlation networks(TCCCNs) were applied to the identification of the powder pharmaceutical samples of metronidazole based on near infrared(NIR) diffuse reflectance spectra. This work focused on the comparison of performances of the uni-output TCCCN(Uni-TCCCN) to multi-output TCCCN(Multi-TCCCN) by using near infrared diffuse reflectance spectra of metronidazole. 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 metronidazole pharmaceutical powders. The results showed that multiple outputs network generally worked better than the single output networks. With proper network parameters the pharmaceutical powders can be classified at a rate of 100% in this work. Also, the effects of neural network parameters including number of candidate nodes, type of transfer functions(linear, sigmoid functions and temperature-constrained sigmoid function, respectively) on classification were discussed.
Temperature-constrained cascade correlation networks(TCCCNs) were applied to the identification of the powder pharmaceutical samples of metronidazole based on near infrared(NIR) diffuse reflectance spectra. This work focused on the comparison of performances of the uni-output TCCCN(Uni-TCCCN) to multi-output TCCCN(Multi-TCCCN) by using near infrared diffuse reflectance spectra of metronidazole. 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 metronidazole pharmaceutical powders. The results showed that multiple outputs network generally worked better than the single output networks. With proper network parameters the pharmaceutical powders can be classified at a rate of 100% in this work. Also, the effects of neural network parameters including number of candidate nodes, type of transfer functions(linear, sigmoid functions and temperature-constrained sigmoid function, respectively) on classification were discussed.
出处
《高等学校化学学报》
SCIE
EI
CAS
CSCD
北大核心
2004年第7期1251-1253,共3页
Chemical Journal of Chinese Universities
基金
北京市教育委员会科学技术发展项目 (批准号 :KM2 0 0 3 10 0 2 810 5 )资助
关键词
甲硝唑
质量控制
温度限制串联相关神经网络
近红外反射光谱法
Temperature-constrained cascade correlation networks
Near infrared reflectance spectrum
Classification
Metronidazole