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. Significant differences in fingerprint pattern between some of the mutants were observed. At the same time, the physical properties of the cell surfaces were determined using microelectrophoresis, contact angle measurements and aqueous two-phase partitioning and compared to the SPR fingerprints. The generated cell surface fingerprints and the physical property data were evaluated with multivariate data analysis that showed that the cells were separated into individual groups in a similar way using principal component analysis plots (PCA).

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.shik1), phosphate limitation resulted in a higher yield of shikimic acid (0.059 +/- 0.012 vs. 0.024 +/- 0.005 c-mol/c-mol) and a lower yield of by-products from the shikimate pathway, when compared to carbon-limited condition. The yield of the by-product 3-dehydroshikimic acid (DHS) decreased from 0.076 +/- 0.028 to 0.022 +/- 0.001 c-mol/c-mol. Several other by-products were only detected under carbon-limited conditions. The latter group included 3-dehydroquinic acid (0.021 +/- 0.021 c-mol/c-mol), quinic acid (0.012 +/- 0.005 c-mol/c-mol), and gallic acid (0.002 +/- 0.001 c-mol/c-mol). For both strains, more acetate was produced under phosphate than the carbon-limited case. Considerable cell lysis was found for both strains but was higher for W3110.shik1, and increased for both strains under phosphate limitation. The advantages of the latter condition in terms of an increased shikimic acid yield was thus counteracted by an increased cell lysis, which may make downstream processing more difficult.

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. The NIR signals were also used to set up empirical partial least-squares (PLS) models for prediction of the cultures' pH and titratable acidity (expressed as Thorner degrees, degrees T). By using five or six PLS factors the models yielded acceptable predictions that could be further improved by increasing the number of reliable and precise calibration data. The presented results demonstrate that the fusion of the NIR and EN signals has a potential for rapid on-line monitoring and assessment of process quality of yogurt 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. The only parameters needed were the substrate feed concentration and the substrate yield of the growth-limiting substrate. On-line near-infrared spectroscopy data were used to demonstrate the performance of the control algorithm on an Escherichia coli fed-batch cultivation for tryptophan production. The controller showed good ability to track a defined biomass trajectory during varying process dynamics. The robustness of the control was high, despite significant external disturbances on the cultivation and control parameters.

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., and were used for processing and carrying out a number of computational tasks such as partial least-square regression, principal component analysis, artificial neural network modelling, heuristic decision-making and adaptive control. The system was demonstrated on different cultivations/fermentations which illustrated sensor fusion control, multivariate statistical process monitoring, adaptive glucose control and adaptive multivariate control. The performance of these examples showed high operational stability and reliable function and meet typical requirements for production safety and quality.

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. The results demonstrate that on-line sensor fusion with the chosen analyzers improves monitoring and quality control of yoghurt fermentation with implications to other fermentation processes.

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. More importantly, PCA also allowed a qualitative assessment of the preculture performance during subsequent fed-batch cultivations. The electronic nose signals from the precultures showed, furthermore, a high correlation to the time of phosphate limitation and the tryptophan yield coefficient of the subsequent fed-batch cultivations, which allowed an accurate prediction of these process variables using partial least squares (PLS). The results demonstrate on data from 12 cultivations how the electronic nose can be a useful tool for the assessment of inoculum quality, thereby providing means of reducing batch-to-batch variation and increasing the productivity of bioprocesses.