Identification and characterization of major degradation products of risperidone in bulk drug and pharmaceutical dosage forms.

Acid, base and oxygen stability of risperidone, a novel anti-psychotic drug, has been evaluated storing the sample in solution phase. One of the major degradation products has been identified and characterized by using techniques namely IR, MS and NMR after isolation by preparative LC. The other major degradation product has been identified with help of MS/MS data and by co-eluting in analytical LC with the available standard. The effect of acid and base resulted in the formation of hydroxy risperidone and the effect of oxygen lead to the formation of N-oxide of risperidone. The two major degradation products in the dosage forms were also characterized as 9-hydroxy risperidone and N-oxide of risperidone, after enrichment through preparative LC, by LC-MS/MS and HPLC. Structural elucidation of degradation product leading to the formation of N-oxide of risperidone is discussed in detail.

Force Parameters for On-line Tool Wear Estimation: A Neural Network Approach.

Reliable on-line tool conditioning monitoring is an essential feature of modern sophisticated and automated machine tools. Appropriate and timely decision for tool-change is urgently required in the machining systems. Ample researches have been carried out in this direction.Recently artificial neural networks (NN) are applied for this purpose in conjunction with suitable sensory systems. Its fast processing capability is well-suited for quick estimation of tool condition and corrective measure to be taken.The present work uses back-propagation type training and feed-forward testing procedures for the neural networks. Three models using different force parameters are tried to monitor tool wear on-line. The close estimation of the modeled output to the actual wear value demonstrates the possibility of successful tool wear monitoring. Copyright 1996 Elsevier Science Ltd.