The application of artificial neural network(ANN) and near-infrared spectroscopy for pharmaceutical nondestructive quantitative analysis of Paracetamol was investigated. The artificial neural network patterns of Parac...The application of artificial neural network(ANN) and near-infrared spectroscopy for pharmaceutical nondestructive quantitative analysis of Paracetamol was investigated. The artificial neural network patterns of Paracetamol tablet medicines, powder medicines, first derivative spectra and second derivative spectra were established, and they were compared each other. The uncertain specimens were predicted. The parameters affecting ANN were discussed. A new network evaluation criterion, i.e., the degree of approximation, was employed, and the predicted results were reliable.展开更多
In this paper, a double artificial neural network （DANN） algorithm was used to parse near infrared （NIR） reflectance spectrum of Cofrel medicines. The contents of benproperine phosphate, which is the effective ing...In this paper, a double artificial neural network （DANN） algorithm was used to parse near infrared （NIR） reflectance spectrum of Cofrel medicines. The contents of benproperine phosphate, which is the effective ingredient in Cofrel medicines, were accurately nondestructive quantitatively predicted. Compared the results with those of HPLC, the relative errors （RE %） were less than 0.18%. The analytical results could be applied to qualitative control of Cofrel medicines.展开更多
文摘The application of artificial neural network(ANN) and near-infrared spectroscopy for pharmaceutical nondestructive quantitative analysis of Paracetamol was investigated. The artificial neural network patterns of Paracetamol tablet medicines, powder medicines, first derivative spectra and second derivative spectra were established, and they were compared each other. The uncertain specimens were predicted. The parameters affecting ANN were discussed. A new network evaluation criterion, i.e., the degree of approximation, was employed, and the predicted results were reliable.
文摘In this paper, a double artificial neural network （DANN） algorithm was used to parse near infrared （NIR） reflectance spectrum of Cofrel medicines. The contents of benproperine phosphate, which is the effective ingredient in Cofrel medicines, were accurately nondestructive quantitatively predicted. Compared the results with those of HPLC, the relative errors （RE %） were less than 0.18%. The analytical results could be applied to qualitative control of Cofrel medicines.
