Bioprocess in-line monitoring and control using Raman spectroscopy and Indirect Hard Modeling (IHM).

Process in-line monitoring and control are crucial to optimize the productivity of bioprocesses. A frequently applied Process Analytical Technology (PAT) tool for bioprocess in-line monitoring is Raman spectroscopy. However, evaluating bioprocess Raman spectra is complex and calibrating state-of-the-art statistical evaluation models is effortful. To overcome this challenge, we developed an Indirect Hard Modeling (IHM) prediction model in a previous study. The combination of Raman spectroscopy and the IHM prediction model enables non-invasive in-line monitoring of glucose and ethanol mass fractions during yeast fermentations with significantly less calibration effort than comparable approaches based on statistical models. In this study, we advance this IHM-based approach and successfully demonstrate that the combination of Raman spectroscopy and IHM is capable of not only bioprocess monitoring but also bioprocess control. For this purpose, we used this combination's in-line information as input of a simple on-off glucose controller to control the glucose mass fraction in Saccharomyces cerevisiae fermentations. When we performed two of these fermentations with different predefined glucose set points, we achieved similar process control quality as approaches using statistical models, despite considerably smaller calibration effort. Therefore, this study reaffirms that the combination of Raman spectroscopy and IHM is a powerful PAT tool for bioprocesses.

The Good pH probe: non-invasive pH in-line monitoring using Good buffers and Raman spectroscopy.

In bioprocesses, the pH value is a critical process parameter that requires monitoring and control. For pH monitoring, potentiometric methods such as pH electrodes are state of the art. However, they are invasive and show measurement value drift. Spectroscopic pH monitoring is a non-invasive alternative to potentiometric methods avoiding this measurement value drift. In this study, we developed the Good pH probe, which is an approach for spectroscopic pH monitoring in bioprocesses with an effective working range between pH 6 and pH 8 that does not require the estimation of activity coefficients. The Good pH probe combines for the first time the Good buffer 3-(N-morpholino)propanesulfonic acid (MOPS) as pH indicator with Raman spectroscopy as spectroscopic technique, and Indirect Hard Modeling (IHM) for the spectral evaluation. During a detailed characterization, we proved that the Good pH probe is reversible, exhibits no temperature dependence between 15 and 40 °C, has low sensitivity to the ionic strength up to 1100 mM, and is applicable in more complex systems, in which other components significantly superimpose the spectral features of MOPS. Finally, the Good pH probe was successfully used for non-invasive pH in-line monitoring during an industrially relevant enzyme-catalyzed reaction with a root mean square error of prediction (RMSEP) of 0.04 pH levels. Thus, the Good pH probe extends the list of critical process parameters monitorable using Raman spectroscopy and IHM by the pH value.

Multicomponent diffusion coefficients from microfluidics using Raman microspectroscopy.

Diffusion is slow. Thus, diffusion experiments are intrinsically time-consuming and laborious. Additionally, the experimental effort is multiplied for multicomponent systems as the determination of multicomponent diffusion coefficients typically requires several experiments. To reduce the experimental effort, we present the first microfluidic diffusion measurement method for multicomponent liquid systems. The measurement setup combines a microfluidic chip with Raman microspectroscopy. Excellent agreement between experimental results and literature data is achieved for the binary system cyclohexane + toluene and the ternary system 1-propanol + 1-chlorobutane + heptane. The Fick diffusion coefficients are obtained from fitting a multicomponent convection-diffusion model to the mole fractions measured in experiments. Ternary diffusion coefficients can be obtained from a single experiment; high accuracy is already obtained from two experiments. Advantages of the presented measurement method are thus short measurement times, reduced sample consumption, and less experiments for the determination of a multicomponent diffusion coefficient.