Real-Time Monitoring of Bioethanol Fermentation with Industrial Musts Using Mid-Infrared Spectroscopy

On-line targeted metabolomics for real-time monitoring of relevant compounds in fermentation processes

Fermentation monitoring is a powerful tool for bioprocess development and optimization. On-line metabolomics is a technology that is starting to gain attention as a bioprocess monitoring tool, allowing the direct measurement of many compounds in the fermentation broth at a very high time resolution. In this work, targeted on-line metabolomics was used to monitor 40 metabolites of interest during three Escherichia coli succinate production fermentation experiments every 5 min with a triple quadrupole mass spectrometer. This allowed capturing high-time resolution biological data that can provide critical information for process optimization. For nine of these metabolites, simple univariate regression models were used to model compound concentration from their on-line mass spectrometry peak area. These on-line metabolomics univariate models performed comparably to vibrational spectroscopy multivariate partial least squares regressions models reported in the literature, which typically are much more complex and time consuming to build. In conclusion, this work shows how on-line metabolomics can be used to directly monitor many bioprocess compounds of interest and obtain rich biological and bioprocess data.

Segregationally stabilised plasmids improve production of commodity chemicals in glucose-limited continuous fermentation

Background

The production of chemicals via bio-based routes is held back by limited easy-to-use stabilisation systems. A wide range of plasmid stabilisation mechanisms can be found in the literature, however, how these mechanisms effect genetic stability and how host strains still revert to non-productive variants is poorly understood at the single-cell level. This phenomenon can generate difficulties in production-scale bioreactors as different populations of productive and non-productive cells can arise. To understand how to prevent non-productive strains from arising, it is vital to understand strain behaviour at a single-cell level.

The persistence of genes located on plasmid vectors is dependent on numerous factors but can be broadly separated into structural stability and segregational stability. While structural stability refers to the capability of a cell to resist genetic mutations that bring about a loss of gene function in a production pathway, segregational stability refers to the capability of a cell to correctly distribute plasmids into daughter cells to maintain copy number. A lack of segregational stability can rapidly generate plasmid-free variants during replication, which compromises productivity.

Results

Citramalate synthase expression was linked in an operon to the expression of a fluorescent reporter to enable rapid screening of the retention of a model chemical synthesis pathway in a continuous fermentation of E. coli. Cells without additional plasmid stabilisation started to lose productivity immediately after entering the continuous phase. Inclusion of a multimer resolution site, cer, enabled a steady-state production period of 58 h before a drop in productivity was detected. Single-cell fluorescence measurements showed that plasmid-free variants arose rapidly without cer stabilisation and that this was likely due to unequal distribution of plasmid into daughter cells during cell division. The addition of cer increased total chemical yield by more than 50%.

Conclusions

This study shows the potential remains high for plasmids to be used as pathway vectors in industrial bio-based chemicals production, providing they are correctly stabilised. We demonstrate the need for accessible bacterial ‘toolkits’ to enable rapid production of known, stabilised bacterial production strains to enable continuous fermentation at scale for the chemicals industry.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01958-3.

On-line untargeted metabolomics monitoring of an E. coli succinate fermentation process

The real‐time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on‐line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreactor every 5 min via flow injection mass spectrometry. The travel time from the bioreactor to the mass spectrometer was 30 s. Using mass spectrometry allows, on the one hand, the direct monitoring of targeted key process compounds of interest and, on the other hand, provides information on hundreds of additional untargeted compounds without requiring previous calibration data. In this study, this technology was applied in an Escherichia coli succinate fermentation process and 886 different m/z signals were monitored, including key process compounds (glucose, succinate, and pyruvate), potential biomarkers of biomass formation such as (R)‐2,3‐dihydroxy‐isovalerate and (R)‐2,3‐dihydroxy‐3‐methylpentanoate and compounds from the pentose phosphate pathway and nucleotide metabolism, among others. The main advantage of the RTMet technology is that it allows the monitoring of hundreds of signals without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess monitoring and for testing many different strains and process conditions for bioprocess development.

Ethanol yield calculations in biorefineries

The ethanol yield on sugar during alcoholic fermentation allows for diverse interpretation in academia and industry. There are several different ways to calculate this parameter, which is the most important one in this industrial bioprocess and the one that should be maximized, as reported by Pereira, Rodrigues, Sonego, Cruz and Badino (A new methodology to calculate the ethanol fermentation efficiency at bench and industrial scales. Ind Eng Chem Res 2018; 57: 16182-91). On the one hand, the various methods currently employed in industry provide dissimilar results, and recent evidence shows that yield has been consistently overestimated in Brazilian sugarcane biorefineries. On the other hand, in academia, researchers often lack information on all the intricate aspects involved in calculating the ethanol yield in industry. Here, we comment on these two aspects, using fuel ethanol production from sugarcane in Brazilian biorefineries as an example, and taking the work of Pereira, Rodrigues, Sonego, Cruz and Badino (A new methodology to calculate the ethanol fermentation efficiency at bench and industrial scales. Ind Eng Chem Res 2018; 57: 16182-91.) as a starting point. Our work is an attempt to demystify some common beliefs and to foster closer interaction between academic and industrial professionals from the fermentation sector. Pereira, Rodrigues, Sonego, Cruz and Badino (A new methodology to calculate the ethanol fermentation efficiency at bench and industrial scales. Ind Eng Chem Res 2018; 57: 16182-91).

Self-organizing maps-based generalized feature set selection for model adaption without reference data for batch process

When fourier transform infrared spectroscopy (FTIR) techniques combined with multivariate calibration are used to measure the key process features or analyte concentrations during batch process, model adaption is indispensable for maintaining the predictability of a primary calibration model in new secondary batches. Many model adaption methods conforming to the actual application scenario of batch process have been proposed. Here we report on a novel standard-free model adaption method without reference measurement called variable selection strategy with self-organizing maps (VSSOM). It uses self-organizing maps (SOM) to classify the whole spectral variables into multiple classes according to the spectra from primary batch and secondary batch, respectively; and the corresponding primary feature subsets and secondary feature subsets are formed firstly. Secondly, candidate feature subsets without empty elements are generated by operating intersection between any primary feature subsets and any secondary feature subsets. Thirdly, the candidate feature subset with minimum root mean square error of cross-validation (RMSECV) for the primary calibration set is selected as the optimal feature subset. In this manner, the optimal feature subset can be identified from the candidate feature subsets. In other words, VSSOM aims to create a stable and consistent feature subset across different batches provided that it selects better features within the intersection sets between primary feature subsets and any secondary feature subsets. Two batch process datasets (γ-polyglutamic acid fermentation and paeoniflorin extraction) are presented for comparing the VSSOM method with No transfer partial least squares (PLS), boxcar signal transfer (BST), successive projection algorithm (SPA), transfer component analysis (TCA) and domain-invariant iterative partial least squares (DIPALS). Experimental results show that VSSOM has superior performance and comparable prediction performance in all the scenarios.