A combined approach of generalized additive model and bootstrap with small sample sets for fault diagnosis in fermentation process of glutamate

摘要

BackgroundGlutamate is of great importance in food and pharmaceutical industries. There is still lack of effective statistical approaches for fault diagnosis in the fermentation process of glutamate. To date, the statistical approach based on generalized additive model (GAM) and bootstrap has not been used for fault diagnosis in fermentation processes, much less the fermentation process of glutamate with small samples sets.