Sensor fusion with on-line gas emission multisensor arrays and standard process measuring devices in baker's yeast manufacturing process.

The use of a multisensor array for measuring the emission from a production-scale baker's yeast manufacturing process is reported. The sensor array, containing 14 different gas-sensitive semiconductor devices and an infrared gas sensor, was used to monitor the gas emission from a yeast culture bioreactor during fed-batch operation. The signal pattern from the sensors was evaluated in relation to two key process variables, the cell mass and the ethanol concentrations. Fusion with the on-line sensor signals for reactor weight and aeration rate made it possible to estimate cell mass and ethanol concentration using computation with backpropagating artificial neural nets. Identification of process states with the same fusion of sensor signals was realized using principal component analysis. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 55: 427-438, 1997.

[1]  Sven-Olof Enfors,et al.  Simulation of the dynamics in the Baker's yeast process , 1990 .

[2]  P. Hagander,et al.  Experience in using an ethanol sensor to control molasses feed-rates in baker's yeast production , 1988 .

[3]  D B Kell,et al.  Rapid screening for metabolite overproduction in fermentor broths, using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks , 1994, Biotechnology and bioengineering.

[4]  K. Persaud,et al.  Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose , 1982, Nature.

[5]  G. Gauglitz,et al.  Simultaneous determination of penicillin and ampicillin by spectral fibre-optical enzyme optodes and multivariate data analysis based on transient signals obtained by flow injection analysis. , 1995, Talanta.

[6]  J. Gardner.  Detection of vapours and odours from a multisensor array using pattern recognition Part 1. Principal component and cluster analysis , 1991 .

[7]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[8]  Ingemar Lundström,et al.  From hydrogen sensors to olfactory images — twenty years with catalytic field-effect devices , 1993 .

[9]  Ingemar Lundström,et al.  Field Effect Gas Sensors , 1992 .

[10]  Hans Sundgren,et al.  Electronic nose for odor classification of grains , 1996 .

[11]  Ingemar Lundström,et al.  Catalytic metals and field-effect devices—a useful combination , 1990 .

[12]  K B Konstantinov,et al.  Monitoring and control of the physiological state of cell cultures. , 2000, Biotechnology and bioengineering.

[13]  D Matteuzzi,et al.  A near‐infrarod spectroscopy technique for the control of fermentation processes: An application to lactic acid fermentation , 1994, Biotechnology and bioengineering.

[14]  I Lundström,et al.  A multisensor array for visualizing continuous state transitions in biopharmaceutical processes using principal component analysis. , 1998, Biosensors & bioelectronics.

[15]  Ingemar Lundström,et al.  Monitoring sausage fermentation using an electronic nose , 1998 .

[16]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .

[17]  Norio Miura,et al.  New Approaches in the Design of Gas Sensors , 1992 .