Key biochemical parameters are hard to measure online in Nosiheptide fermentation process.This brings difficulties to control and optimize Nosiheptide fermentation process.To solve this problem

softsensor technique was used

and a staged modeling method was proposed.Firstly

based on the staged unstructured model of Nosiheptide fermentation process

secondary variables were selected according to the implicit function existence theorem.Secondly

the modeling data were classified according to their stages by using fuzzy cmeans clustering algorithm.Lastly

a model for the online identification of fermentation stages and some local softsensor models were developed by using neural network.The testing results showed the effectiveness of the proposed approach.