A method for microbial cell surface fingerprinting based on surface plasmon resonance.

A method for microbial cell surface fingerprinting using surface plasmon resonance (SPR) is suggested. Four different Escherichia coli mutants have been used as model cells. Cell surface fingerprints were generated by registration of the interaction between the cell mutants and four different surfaces, with different physical and chemical properties, when a cell suspension was flown over the surface.

Shikimic acid production by a modified strain of E. coli (W3110.shik1) under phosphate-limited and carbon-limited conditions.

Shikimic acid is one of several industrially interesting chiral starting materials formed in the aromatic amino acid pathway of plants and microorganisms. In this study, the physiology of a shikimic acid producing strain of Escherichia coli (derived from W3110) deleted in aroL (shikimic acid kinase II gene), was compared to that of a corresponding control strain (W3110) under carbon- and phosphate-limited conditions. For the shikimic acid producing strain (referred to as W3110.

On-line multisensor monitoring of yogurt and filmjölk fermentations on production scale.

Near-infrared (NIR) spectrometry and electronic nose (EN) data were used for on-line monitoring of yogurt and filmjölk (a Swedish yogurt-like sour milk) fermentations under industrial conditions. The NIR and EN signals were selected by evaluation of principal component analysis loading vectors and further analyzed by studying the variability of the selected principal components. First principal components for the NIR and the EN signals were used for on-line generation of a process trajectory plot visualizing the actual state of fermentation.

Bioprocess control from a multivariate process trajectory.

A multivariate bioprocess control approach, capable of tracking a pre-set process trajectory correlated to the biomass or product concentration in the bioprocess is described. The trajectory was either a latent variable derived from multivariate statistical process monitoring (MSPC) based on partial least squares (PLS) modeling, or the absolute value of the process variable. In the control algorithm the substrate feed pump rate was calculated from on-line analyzer data.

Integration of distributed multi-analyzer monitoring and control in bioprocessing based on a real-time expert system.

A computer system solution for integration of a distributed bioreactor monitoring and control instrumentation on the laboratory scale is described. Bioreactors equipped with on-line analyzers for mass spectrometry, near-infrared spectroscopy, electrochemical probes and multi-array gas sensors and their respective software were networked through a real-time expert systems platform. The system allowed data transmission of more than 1800 different signals from the instrumentation, including signals from gas sensors, electrodes, spectrometer detectors, balances, flowmeters, etc.

Sensor fusion for on-line monitoring of yoghurt fermentation.

Measurement data from an electronic nose (EN), a near-infrared spectrometer (NIRS) and standard bioreactor probes were used to follow the course of lab-scale yoghurt fermentation. The sensor signals were fused using a cascade neural network: a primary network predicted quantitative process variables, including lactose, galactose and lactate; a secondary network predicted a qualitative process state variable describing critical process phases, such as the onset of coagulation or the harvest time. Although the accuracy of the neural network prediction was acceptable and comparable with the off-line reference assay, its stability and performance were significantly improved by correction of faulty data.

Assessment of the performance of a fed-batch cultivation from the preculture quality using an electronic nose.

An electronic nose, a gas-phase multisensor system, was used to monitor precultivations of a recombinant tryptophan-producing Escherichia coli strain. The electronic nose signals showed a high correlation toward the main stages of the precultivations, namely, exponential growth, oxygen-limited growth, and glucose depletion. Principal component analysis (PCA) of the electronic nose signals was performed and shown to be useful for monitoring preculture progression.