Artifical Neural Networks

An Artificial Neural Network (ANN) is a regressive machine learning algorithm composed of layered interconnected nodes. Essentially, this technique allows the estimation of process variables without the requirement of a process model. ANN are used to provide approximations to non-linear systems. Briefly, the network must receive an input from a set of data e.g. pH, NO3 concentration; which is then propagated by a hidden or intermediate layer to an outer layer, which finally predicts the variable of interest, e.g. biomass concentration. ANN are considered highly flexible models since they provide the possibility to handle large amounts of parameters and demonstrably approximate any function.

·    Jung, S.K., Lee, S.B., 2006. In situ monitoring of cell concentration in a photobioreactor using image analysis: Comparison of uniform light distribution model and artificial neural networks. Biotechnol. Prog. 22, 1443–1450.

·       Liu, J.Y., Zeng, L.H., Ren, Z.H., Du, T.M., Liu, X., 2020. Rapid in situ measurements of algal cell concentrations using an artificial neural network and single-excitation fluorescence spectrometry. Algal Res. 45.

·   López Expósito, P., Blanco Suárez, A., Negro Álvarez, C., 2017. Laser reflectance measurement for the online monitoring of Chlorella sorokiniana biomass concentration. Journal of Biotechnology 243, 10–15.

·       Melcher, M., Scharl, T., Spangl, B., Luchner, M., Cserjan, M., Bayer, K., Leisch, F., Striedner, G., 2015. The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed-batch fermentations. Biotechnology Journal 10, 1770–1782.

·     Rudolph G, Gierse A, Lindner P, Kasper C, Hitzmann B, Scheper T. 2007. Observation and analysis of lab scaled microcarrier cultivation by in-situ microscopy with image processing tools. Springer, R. Smith (ed.). Cell Technology for Cell Products (Proceedings of the 19th ESACT Meeting, Harrogate, UK June 5-8 2005), 657-661.

·     Rudolph, G., Lindner, P., Gierse, A., Bluma, A., Martinez, G., Hitzmann, B., Scheper, T., 2008. Online monitoring of microcarrier based fibroblast cultivations with in situ microscopy. Biotechnol Bioeng 99, 136–145.

·      Seiler, L.K., Phung, N.L., Nikolin, C., Immenschuh, S., Erck, C., Kaufeld, J., Haller, H., Falk, C.S., Jonczyk, R., Lindner, P., Thoms, S., Siegl, J., Mayer, G., Feederle, R., Blume, C.A., 2022. An antibody-aptamer-hybrid lateral flow assay for detection of CXCL9 in antibody-mediated rejection after kidney transplantation. Diagnostics (Basel) 12, 308.

·       Yang, J., Huang, Y., Xu, H., Gu, D., Xu, F., Tang, J., Fang, C., Yang, Y., 2020. Optimization of fungi co-fermentation for improving anthraquinone contents and antioxidant activity using artificial neural networks. Food Chemistry 313, 126138.

·    Zhang, A.H., Zhu, K.Y., Zhuang, X.Y., Liao, L.X., Huang, S.Y., Yao, C.Y., Fang, B.S., 2020. A robust soft sensor to monitor 1,3-propanediol fermentation process by Clostridium butyricum based on artificial neural network. Biotechnol. Bioeng. 117, 3345–3355.


Specialist for Artifical Neural Networks

Dr. Patrick Lindner

Institute of Technical Chemistry
Leibniz University of Hannover
Callinstraße 5
30167 Hannover, Germany
Tel.: + 49 511 762 2253
Fax: + 49 511 762 3004