The monitoring of microbiological processes using Raman spectroscopy has gained in importance over the past few years. Commercial Raman spectroscopic equipment consists of a laser, spectrometer, and fiberoptic immersion probe in direct contact with the fermentation medium. To avoid possible sterilization problems and biofilm formation on the probe tip, a large-aperture Raman probe was developed. The design of the probe enables non-contact in-line measurements through glass vessels or inspection glasses of bioreactors and chemical reactors. The practical applicability of the probe was tested during yeast fermentations by monitoring the consumption of substrate glucose and the formation of ethanol as the product. Multiple linear regression models were applied to evaluate the Raman spectra. Reference values were determined by high-performance liquid chromatography. The relative errors of prediction for glucose and ethanol were 5 and 3%, respectively. The presented Raman probe allows simple adaption to a wide range of processes in the chemical, pharmaceutical, and biotechnological industries.

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Control and optimization of production processes in the chemical, pharmaceutical, and beverage industries require real-time monitoring of characteristic parameters, such as concentrations of educts and products [1,2]. For a wide field of applications chemical sensors and optical spectroscopy are non-invasive tools to investigate processes without sample preparation in contrast to other analytical methods like mass spectrometry or high performance liquid chromatography. The short measurement time in the order of seconds permits on-line process control. In recent years, chemical sensors have taken over many measuring tasks due to their compactness and short response time [3,4]. For example, Dickert et al [5] used a mass-sensitive quartz-microbalance (QMB) to monitor the formation of a Grignard reagent in a chemical reaction. There is still strong demand to develop analyte specific sensors which exhibit low cross-sensitivities to other compounds [4]. However, molecules can be distinguished by their infrared (IR) spectra. Therefore, near infrared (NIR) and mid-infrared (MIR) spectroscopy enable simultaneous determination of several compounds and are

Raman and mid-infrared (MIR) spectroscopy are useful tools for the specific detection of molecules, since both methods are based on the excitation of fundamental vibration modes. In this study, Raman and MIR spectroscopy were applied simultaneously during aerobic yeast fermentations of Saccharomyces cerevisiae. Based on the recorded Raman intensities and MIR absorption spectra, respectively, temporal concentration courses of glucose, ethanol, and biomass were determined. The chemometric methods used to evaluate the analyte concentrations were partial least squares (PLS) regression and multiple linear regression (MLR). In view of potential photometric sensors, MLR models based on two (2D) and four (4D) analyte-specific optical channels were developed. All chemometric models were tested to predict glucose concentrations between 0 and 30 g L−1, ethanol concentrations between 0 and 10 g L−1, and biomass concentrations up to 15 g L−1 in real time during diauxic growth. Root-mean-squared errors of prediction (RMSEP) of 0.68 g L−1, 0.48 g L−1, and 0.37 g L−1 for glucose, ethanol, and biomass were achieved using the MIR setup combined with a PLS model. In the case of Raman spectroscopy, the corresponding RMSEP values were 0.92 g L−1, 0.39 g L−1, and 0.29 g L−1. Nevertheless, the simple 4D MLR models could reach the performance of the more complex PLS evaluation. Consequently, the replacement of spectrometer setups by four-channel sensors were discussed. Moreover, the advantages and disadvantages of Raman and MIR setups are demonstrated with regard to process implementation.

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