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.

Interdisciplinary development of an overall process concept from glucose to 4,5-dimethyl-1,3-dioxolane via 2,3-butanediol.

To reduce carbon dioxide emissions, carbon-neutral fuels have recently gained renewed attention. Here we show the development and evaluation of process routes for the production of such a fuel, the cyclic acetal 4,5-dimethyl-1,3-dioxolane, from glucose via 2,3-butanediol. The selected process routes are based on the sequential use of microbes, enzymes and chemo-catalysts in order to exploit the full potential of the different catalyst systems through a tailor-made combination. The catalysts (microbes, enzymes, chemo-catalysts) and the reaction medium selected for each conversion step are key factors in the development of the respective production methods. The production of the intermediate 2,3-butanediol by combined microbial and enzyme catalysis is compared to the conventional microbial route from glucose in terms of specific energy demand and overall yield, with the conventional route remaining more efficient. In order to be competitive with current 2,3-butanediol production, the key performance indicator, enzyme stability to high aldehyde concentrations, needs to be increased. The target value for the enzyme stability is an acetaldehyde concentration of 600 mM, which is higher than the current maximum concentration (200 mM) by a factor of three.

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.

Bioprocess in-line monitoring using Raman spectroscopy and Indirect Hard Modeling (IHM): A simple calibration yields a robust model.

To increase the process productivity and product quality of bioprocesses, the in-line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in-situ and non-invasively. However, evaluating bioprocess Raman spectra with a robust state-of-the-art statistical model requires effortful model calibration. In the present study, we in-line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics-based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra.

Completely Computational Model Setup for Spectroscopic Techniques: The Ab Initio Molecular Dynamics Indirect Hard Modeling Approach.

The spectroscopic quantification of mixture compositions usually requires pure compounds and mixtures of known compositions for calibration. Since they are not always available, methods to fill such gaps have evolved, which are, however, not generally applicable. Therefore, calibration can be extremely challenging, especially when multiple unstable species, for example, intermediates, exist in a system. This study presents a new calibration approach that uses ab initio molecular dynamics (AIMD)-simulated spectra to set up and calibrate models for the physics-based spectral analysis method indirect hard modeling (IHM). To demonstrate our approach called AIMD-IHM, we analyze Raman spectra of ternary hydrogen-bonding mixtures of acetone, methanol, and ethanol. The derived AIMD-IHM pure-component models and calibration coefficients are in good agreement with conventionally generated experimental results. The method yields compositions with prediction errors of less than 5% without any experimental calibration input. Our approach can be extended, in principle, to infrared and NMR spectroscopy and allows for the analysis of systems that were hitherto inaccessible to quantitative spectroscopic analysis.

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.

Method for quantitative determination of spatial polymer distribution in alginate beads using Raman spectroscopy.

A new method based on Raman spectroscopy is presented for non-invasive, quantitative determination of the spatial polymer distribution in alginate beads of approximately 4 mm diameter. With the experimental setup, a two-dimensional image is created along a thin measuring line through the bead comprising one spatial and one spectral dimension. For quantitative analysis of the Raman spectra, the method of indirect hard modeling was applied to make use of the information contained in the entire recorded spectra. For quantification of the alginate signals from within the beads, a calibration curve acquired from sodium alginate solutions was used after it was shown that only negligible differences occur between signals from alginate solutions and alginate gels. The distribution of alginate over the bead gel matrix was acquired with high spatial (51 microm) and time (12 s) resolution. The inhomogeneous distribution obtained using the new measuring technique is qualitatively in excellent agreement with data from the literature. In contrast to known measuring techniques, correct quantitative information about the spatial polymer distribution within the matrix was derived. It gave an alginate mass fraction of approximately 0.045 g/g at the edges and 0.02 g/g in the center of the beads. Next to the determination of mere polymer concentrations, the excellent time resolution of the presented method will enable investigation of the dynamic process of gel formation and it will also serve as a basis for investigation of mass transfer of small diffusing molecules in alginate matrices.

Indirect spectral hard modeling for the analysis of reactive and interacting mixtures.

We present an indirect hard modeling (IHM) approach for the quantitative analysis of reactive multicomponent mixtures with intermolecular interaction. It can be used when it is not possible to obtain calibration data in the composition region of interest. The goal of this work, specifically, is to analyze reactive systems, although the validation of the method is done with nonreactive systems. Compared to conventional hard modeling, the new approach reduces the manual work required for modeling and renders unnecessary the assignment of bands in mixture spectra to individual components. It is based on parametric models of the pure component spectra that are made just flexible enough to fit the spectra of the unknown mixtures, and it only requires small calibration data sets that may lie in different regions of the composition space. The application to infrared (IR) and Raman spectra of multicomponent systems is discussed.