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V. Champreda

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Aug 2026

Tailored low-waste integrated biorefinery via hydrothermal-organosolv pretreatment for multiple bio-based products: process performance and techno-economic assessment.

Lignocellulosic biomass is a renewable carbon source with strong potential to partially replace fossil resources. However, its valorization into high-value products remains constrained by technical, economic, and environmental challenges. Fractionation is therefore a critical step in biorefinery design to enable efficient utilization of cellulose, hemicellulose, and lignin. This study developed a sequential hydrothermal (HT)-Organosolv fractionation process of rice straw. The strategy produces fermentable sugars, xylooligosaccharides (XOS) and lignin co-products for industrial applications. Under optimized conditions, the process achieved an XOS production of 18.82 g/L, and a fermentable sugar yield of 618.6 g/kg biomass, with an enzymatic hydrolysis efficiency of 94.2%. Simultaneously, the organosolv step facilitated effective lignin recovery. The extracted lignin retained its characteristic aromatic structure (H/G/S units), highlighting its potential as a bioactive co-product for further valorization. The process demonstrated an energy efficiency of 724.3 g fermentable sugars per kWh, while E-factor was in a range of 0.09-0.12. In addition, a techno-economic assessment was conducted for multiple product pathways, including lactic acid, xylooligosaccharides, and organosolv lignin. Overall, the integration of HT and organosolv pretreatments improve sugar yield, enables lignin recovery, reduce energy demand, and minimize waste, offering a promising approach for sustainable lignocellulosic biorefineries.

Pollawat Charoenkool, S. Sunar, V. Champreda et al. · 0 citations
Open access Aug 2026

Integrating Machine Learning and Transcriptomics to Enhance β‑Carotene Production in Saccharomyces cerevisiae

Optimization of microbial production by synthetic biology is essential for industrial and sustainable biotechnology applications. β-carotene is a high-value compound that can be heterologously produced in the budding yeast Saccharomyces cerevisiae, providing an alternative to natural extraction. In this study, we aimed to enhance β-carotene production by machine learning–guided combinatorial strain engineering and transcriptomic analyses. We fine-tuned the expression of rate-limiting enzymes in the mevalonate (MVA) pathway through combinatorial engineering of promoters and terminators. XGBoost was applied to the Design-Build-Test-Learn (DBTL) cycle to facilitate rapid optimization. In the second DBTL cycle of 1 mL culture screening, fine-tuning MVA gene expression resulted in a 139% improvement in β-carotene titer. Additionally, guided by transcriptomic insights into altered expression of iron uptake genes, we supplemented β-carotene production cultures with iron, resulting in a 70.54% increase in β-carotene titer. Furthermore, integrating the fine-tuned MVA cassette with iron supplementation in 250 mL shake-flasks yielded up to 72.07 mg/L of β-carotene at 72 h, representing a 67.79% increase compared to the β-carotene-producing strain without MVA gene fine-tuning. Our study demonstrates the effectiveness of XGBoost in predicting complex combinatorial designs and highlights the potential of combining machine learning and transcriptomic insights to optimize non-native biochemical production in yeast.

Peerapat Khamwachirapithak, K. Sae-tang, Suriyaporn Bubphasawan et al. · 0 citations