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.

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.

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Multivariate statistical process control (MSPC) was for the first time applied to analyse data from a bioprocess on-line multi-analyser system consisting of an electronic nose (EN), a near-infrared spectroscope (NIRS), a mass spectrometer (MS) and standard bioreactor probes. One hundred and fifty sensor signals from the electronic nose, 1050 wavelength signals from the NIRS, carbon dioxide evolution rate calculated from mass spectrometer signals and standard bioreactor data (eg amount of substrate fed) were interrogated for their ability to model a bioprocess using MSPC. The models obtained were validated on a recombinant Escherichia coli fed-batch process for tryptophan production. Limiting trajectories were defined in the MSPC models for warning, action, and process experience with respect to biomass and tryptophan concentrations. The results showed the capacity and robustness of MSPC models for monitoring with multi-analysers and allowed a comparison of the different analysers' suitability for this kind of data processing. Furthermore, the results demonstrate that MSPC models provide a functional and versatile framework for coping with large information flows and are also suited to a variety of other bioprocessing monitoring and control tasks.

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