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This article presents a novel method for selective acquisition of Fourier transform infrared (FT-IR) spectra of microorganisms in-line during fermentation, using Saccharomyces cerevisiae as an example. The position of the cells relative to the sensitive region of the attenuated total reflection (ATR) FT-IR probe was controlled by combing a commercially available ATR in-line probe with contact-free, gentle particle manipulation by ultrasonic standing waves. A prototype probe was successfully constructed, assembled, and tested in-line during fed-batch fermentations of S. cerevisiae. Control over the position of the cells was achieved by tuning the ultrasound frequency: 2.41 MHz was used for acquisition of spectra of the cells (pushing frequency fp) and 1.87 MHz, for retracting the cells from the ATR element, therefore allowing spectra of the medium to be acquired. Accumulation of storage carbohydrates (trehalose and glycogen) inside the cells was induced by a lack of a nitrogen source in the feed medium. These changes in biochemical composition were visible in the spectra of the cells recorded in-line during the application of fp and could be verified by reference spectra of dried cell samples recorded off-line with a FT-IR microscope. Comparison of the cell spectra with spectra of trehalose, glycogen, glucose, and mannan, i.e., the major carbohydrates present in S. cerevisiae, and principal components analysis revealed that the changes observed in the cell spectra correlated well with the bands specific for trehalose and glycogen. This proves the applicability and capability of ultrasound-enhanced in-line ATR mid-IR spectroscopy as a real-time PAT method for the in situ monitoring of cellular biochemistry during fermentation.

A fast and simple method to control variations in carbohydrate composition of Saccharomyces cerevisiae, baker's yeast, during fermentation was developed using mid-infrared (mid-IR) spectroscopy. The method allows for precise and accurate determinations with minimal or no sample preparation and reagent consumption based on mid-IR spectra and partial least squares (PLS) regression. The PLS models were developed employing the results from reference analysis of the yeast cells. The reference analyses quantify the amount of trehalose, glucose, glycogen, and mannan in S. cerevisiae. The selection and optimization of pretreatment steps of samples such as the disruption of the yeast cells and the hydrolysis of mannan and glycogen to obtain monosaccharides were carried out. Trehalose, glucose, and mannose were determined using high-performance liquid chromatography coupled with a refractive index detector and total carbohydrates were measured using the phenol–sulfuric method. Linear concentration range, accuracy, precision, LOD and LOQ were examined to check the reliability of the chromatographic method for each analyte.FigureComparison of workflows for carbohydrate determination in S.cerevisiae by FT-IR spectroscopy and HPLC-RIElectronic supplementary materialThe online version of this article (doi:10.1007/s00216-013-7239-9) contains supplementary material, which is available to authorized users.

We employed a broadly tunable pulsed external cavity (EC)-QC laser with a spectral tuning range from 1030 cm -1 to 1230 cm -1 and a tuning speed of 166 cm -1 /s for direct absorption spectroscopy of aqueous solutions. The laser offered spectral power densities of up to four orders of magnitude higher than available with a conventional FTIR spectrometer. Therefore, a portable demonstration system with a large optical path length transmission flow cell (165 µm) could be realized preventing clogging of the flow cell. In pulsed mode an EC-QC laser provides significantly higher peak power levels than in continuous-wave mode, but pulse-to-pulse intensity variations, intra-pulse mode hops and mechanical imperfections of the scanning mechanism significantly impair the quality of resulting absorbance spectra. This article reports on measures which we found appropriate to reduce the initially high noise level of EC-QC laser absorbance spectra. These measures include a spectral self-referencing algorithm that makes use of the inherent structure of the EC-QC laser's gain curve to correct laser instabilities, as well as Fourier filtering, among others. This enabled us to derive infrared spectra which were finally useful for quantitative analysis in blood plasma samples. Finally, with the appropriate measures in place and using partial least squares regression analysis it was possible to simultaneously quantify 6 blood analytes from a single physical measurement of a 200 µL blood sample. This proves the potential of EC-QC lasers for practical application in clinical point of care analysis.

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