RESUMEN

The dataset consists of FTIR spectra of ultra-filtered spent sulphite liquor (UF-SSL) from softwood pulping obtained from one paper mill biorefinery plant with the purpose of real-time quantification of the sugar content of UF-SSL. Data collection was performed using a submerged mid-IR probe placed in a continuously stirred tank reactor and reference sugar measurements were performed using HPLC. Spectra were obtained of raw and spiked UF-SSL. As "low complexity" case 25% UF-SSL from one batch was analysed for its 3 most abundant sugars (mannose, xylose, glucose) and as "high complexity" case 25/50/75% UF-SSL from 2 batches was analysed for its 5 most abundant sugars (the latter + galactose, arabinose). In both cases, independent single sugar spikes and simultaneous multiple sugar spikes were performed. Real time in-line data was generated by stepwise and gradual changes in sugar composition over time with a run time of >200 h.

RESUMEN

In this study, a bioprocessing strategy was designed to valorize ultra-filtered spent sulfite liquor (UF-SSL) without prior detoxification steps as well as using it purely as a carbon source supplement to defined or complex media. Hence, a minimal medium for the bioconversion of UF-SSL with Corynebacterium glutamicum was developed and process robustness and reproducibility were validated. Process quantifiability was ensured by development of a biomass measurement technique for matrices with high water-insoluble solids and verified using elemental balancing. Mechanistic modeling based on Monod equations was used to identify batch kinetics. In a final step, scale-up of the developed process was performed to showcase process maturity towards commercialisation.

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RESUMEN

For a sustainable economy, biorefineries that use second-generation feedstocks to produce biochemicals and biofuels are essential. However, the exact composition of renewable feedstocks depends on the natural raw materials used and is therefore highly variable. In this contribution, a process analytical technique (PAT) strategy for determining the sugar composition of lignocellulosic process streams in real-time to enable better control of bioprocesses is presented. An in-line mid-IR probe was used to acquire spectra of ultra-filtered spent sulfite liquor (UF-SSL). Independent partial least squares models were developed for the most abundant sugars in the UF-SSL. Up to 5 sugars were quantified simultaneously to determine the sugar concentration and composition of the UF-SSL. The lowest root mean square errors of the predicted values obtained per analyte were 1.02 g/L arabinose, 1.25 g/L galactose, 0.50 g/L glucose, 1.60 g/L mannose, and 0.85 g/L xylose. Equipped with this novel PAT tool, new bioprocessing strategies can be developed for UF-SSL.

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RESUMEN

Determining total protein content is a routine operation in many laboratories. Despite substantial work on assay optimization interferences, the widely used bicinchoninic acid (BCA) assay remains widely recognized for its robustness. Especially in the field of bioprocess engineering the inaccuracy caused by interfering substances remains hardly predictable and not well understood. Since the introduction of the assay, sample pre-treatment by trichloroacetic acid (TCA) precipitation has been indicated as necessary and sufficient to minimize interferences. However, the sample matrix in cultivation media is not only highly complex but also dynamically changing over process time in terms of qualitative and quantitative composition. A significant misestimation of the total protein concentration of bioprocess samples is often observed when following standard work-up schemes such as TCA precipitation, indicating that this step alone is not an adequate means to avoid measurement bias. Here, we propose a modification of the BCA assay, which is less influenced by sample complexity. The dynamically changing sample matrix composition of bioprocessing samples impairs the conventional approach of compensating for interfering substances via a static offset. Hence, we evaluated the use of a correction factor based on an internal spike measurement for the respective samples. Using protein spikes, the accuracy of the BCA protein quantification could be improved fivefold, taking the BCA protein quantification to a level of accuracy comparable to other, more expensive methods. This will allow reducing expensive iterations in bioprocess development to due inaccurate total protein analytics.

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