arXiv subjects

Nipon Sarmah

Publications and source records attributed to Nipon Sarmah.

3 recordsLinked to original sources

arXiv2026

Valorisation of Fermentation Side-Stream for Waste-to-Mycoprotein: Nutrient Composition, Metabolic Insights and Process Optimisation

Fermentation-derived side streams represent an underutilised resource for sustainable protein production. This study investigates the potential of centrate from industrial Fusarium venenatum fermentation as a nutrient source for fungal biomass generation. Following compositional characterisation, a synthetic centrate medium was formulated and evaluated using a Box-Behnken design combined with response surface methodology. Across 46 experimental runs, cell dry weight (CDW) ranged from 0.22 to 3.87 g per liter, demonstrating a strong dependence on nutrient composition. Ammonia and glucose were identified as the dominant factors influencing biomass production, with significant nonlinear effects. The model predicted a maximum CDW of 4.17 g per liter under optimised conditions, which was experimentally validated at 3.99 g per liter. Carbon conversion efficiency reached up to 29.02%, indicating effective substrate utilisation. These findings demonstrate that fermentation-derived centrate can support substantial fungal growth, while highlighting its potential to enhance nutrient recovery and influence the biochemical composition of sustainable mycoprotein.

eess.SY↗

arXiv2026

Kinetics of Mycoprotein Production from Alternative Carbon Substrates

High throughput screening was used to study of the biokinetics of F. venenatum A3/5 cultivation on alternative carbon substrates, including monosaccharides, disaccharides and mixtures relevant to food & beverage, dairy and agricultural waste streams. Expired functional drink from the beverage sector was also assessed as the primary carbon source for mycoprotein production. Growth data was analysed using modified single and multiphase Gompertz models for comparison of maximum specific growth rate and progression milestones across diverse growth regimes. Time-series substrate and byproduct data was analysed using comparative metrics, providing an explanatory basis for the different growth phenotypes observed. Substrate type strongly influenced the apparent carbon allocation strategies, with rapidly consumed sugars such as glucose and sucrose supporting high growth rates, low biomass yield and a high degree of fermentative byproduct formation. Fructose and xylose cultivations led to slower overall growth but higher biomass yield and lower byproduct formation. Galactose and lactose showed distinct dynamics that suggested co-existence of transport and metabolic induction limitations. In all dual-substrate systems, sequential utilisation was observed. However, metabolic inheritance and environmental shift effects were highlighted as potential kinetic limitations. These conditions exhibited stunted diauxic growth and low yield from secondary sugars, with glucose-dominated primary growth significantly reshaping secondary substrate efficiencies relative to their study in silo. The expired functional drink supported highly rapid growth and achieved the highest maximum specific growth rate and biomass titre of all conditions examined, alongside reduced fermentative overflow and enhanced ethanol reassimilation relative to a compositionally matched synthetic control.

physics.bio-ph↗

arXiv2025

Literature Mining System for Nutraceutical Biosynthesis: From AI Framework to Biological Insight

The extraction of structured knowledge from scientific literature remains a major bottleneck in nutraceutical research, particularly when identifying microbial strains involved in compound biosynthesis. This study presents a domain-adapted system powered by large language models (LLMs) and guided by advanced prompt engineering techniques to automate the identification of nutraceutical-producing microbes from unstructured scientific text. By leveraging few-shot prompting and tailored query designs, the system demonstrates robust performance across multiple configurations, with DeepSeekV3 outperforming LLaMA2 in accuracy, especially when domain-specific strain information is included. A structured and validated dataset comprising 35 nutraceutical-strain associations was generated, spanning amino acids, fibers, phytochemicals, and vitamins. The results reveal significant microbial diversity across monoculture and co-culture systems, with dominant contributions from Corynebacterium glutamicum, Escherichia coli, and Bacillus subtilis, alongside emerging synthetic consortia. This AI-driven framework not only enhances the scalability and interpretability of literature mining but also provides actionable insights for microbial strain selection, synthetic biology design, and precision fermentation strategies in the production of high-value nutraceuticals.

q-bio.QM↗