A self‐tuning vision system for monitoring biotechnological processes: I. Application to production of pullulan by Aureobasidium pullulans

A prototype of a self‐tuning vision system (STVS) has been developed to monitor cell population in fermentations. The STVS combines classical image processing techniques, neural networks and fuzzy logic technologies. By combining these technologies the STVS is able to analyze sampled images of the culture. The proposed system can be “tailored” with minimum effort by an expert who can “teach” the system to recognize cells by showing examples of different morphologies. After adaptation, the STVS is able to capture images, isolate the different cells, classify them according to the expert's criteria, and provide the profile of the cell's population. The system was applied to the classification and analysis of Aureobasidium pullulans. The importance of understanding the changes of population distribution during the fermentation and its effect in the production of pullulan are emphasized. The STVS can be used for monitoring and control of the cell population in small research fermentors or in large‐scale production.

[1]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[2]  Hugo Guterman,et al.  Application of principal component analysis to the design of neural networks , 1994, Neural Parallel Sci. Comput..

[3]  L. Harvey,et al.  Morphology of Aureobasidium pullulans during polysaccharide elaboration , 1984 .

[4]  G. Kraepelin,et al.  Factors affecting dimorphism in Mycotypha (Mucorales): a correlation with the fermentation-respiration equilibrium. , 1974, Journal of general microbiology.

[5]  John F. Reid,et al.  Knowledge-based supervision and control of bioprocess with a machine vision-based sensing system☆ , 1994 .

[6]  John F. Reid,et al.  Coupling a machine vision sensor and a neural net supervised controller: controlling microbial cultivations , 1995 .

[7]  S. Yuen.  Pullulan and its applications. , 1974 .

[8]  B. Kristiansen,et al.  Synthesis of polysaccharide by yeast-like forms of Aureobasidium pullulans. , 1985, Biotechnology and bioengineering.

[9]  J. Zupan,et al.  Neural networks: A new method for solving chemical problems or just a passing phase? , 1991 .

[10]  E Keshavarz-Moore,et al.  Estimation of cell volume and biomass of penicillium chrysogenum using image analysis , 1992, Biotechnology and bioengineering.

[11]  Sankar K. Pal,et al.  Self-organization for object extraction using a multilayer neural network and fuzziness measures , 1993, IEEE Trans. Fuzzy Syst..

[12]  S. Møller,et al.  Bacterial growth on surfaces: automated image analysis for quantification of growth rate-related parameters , 1995, Applied and environmental microbiology.

[13]  T. West,et al.  Polysaccharide production by a reduced pigmentation mutant of the fungus Aureobasidium pullulans , 1993 .

[14]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[15]  R. Crang,et al.  An Analysis of Aureobasidium Pullulans Developmental Stages by Means of Scanning Electron Microscopy , 1977 .

[16]  K L Sublette,et al.  Control of a thiobacillus denitrificans bioreactor using machine vision , 1992, Biotechnology and bioengineering.

[17]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[18]  B. J. Catley,et al.  The Extracellular Polysaccharide, Pullulan, Produced by Aureobasidium pullulans: A Relationship between Elaboration Rate and Morphology , 1980 .