Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is contaminated to certain extent

the product quality and yieldwill be seriously affected

leading to a substantial economic loss. Using information fusion based on the Dezer–Smarandache theory

self-recursive wavelet neural network and unscented kalman filter

a novel method for online prediction of contamination is developed. All state variables of culture process involving easy-to-measure and difficult-to-measure variables commonly obtained with soft-sensors present their contamination symptoms. By extracting and fusing latent information fromthe changing trend of each variable

integral and accurate prediction results for contamination can be achieved. Thismakes preventive and correctivemeasures be taken promptly. The field experimental results showthat themethod can be used to detect the contamination in time

reducing production loss and enhancing economic efficiency.