应用于利用合成木质纤维素水解物生产菌体蛋白的高通量参数估计和不确定性分析

IF 6.2 2区 农林科学 Q1 FOOD SCIENCE & TECHNOLOGY
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引用次数: 0

摘要

当前的全球粮食系统产生了大量废物和碳排放,同时加剧了全球饥饿和蛋白质缺乏的影响。本研究旨在通过探索使用木质纤维素农作物残留物作为微生物蛋白质发酵的原料来应对这些挑战,重点是以高蛋白产量和营养质量著称的菌丝菌株 Fusarium venenatum A3/5。我们提出了一种高通量微升批量发酵系统,并将其与分析化学相结合,以生成微生物生长和底物利用的时间序列数据。我们利用引导取样法开发了一个非结构化生物动力学模型,以量化参数估计的不确定性。该模型通过不同葡萄糖-木糖组成的独立数据集进行了验证,以评估其预测性能。结果表明,模型拟合稳健,生物量、葡萄糖和木糖浓度的决定系数高,均方根误差小。估计的参数值有助于深入了解文氏镰刀菌 A3/5 在混合基质培养中的资源利用策略,这与之前的研究结果非常吻合。估计参数之间存在显著的相关性,凸显了参数可识别性方面的挑战。高通量工作流程为生物动力学模型的开发提供了一种新颖、快速的方法,可有效探索微生物的生长动力学和基质利用。这种创新方法直接支持了从木质纤维素水解物中优化微生物蛋白质生产的基础模型的开发,有助于建立更可持续的全球食品系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

High throughput parameter estimation and uncertainty analysis applied to the production of mycoprotein from synthetic lignocellulosic hydrolysates

High throughput parameter estimation and uncertainty analysis applied to the production of mycoprotein from synthetic lignocellulosic hydrolysates
The current global food system produces substantial waste and carbon emissions while exacerbating the effects of global hunger and protein deficiency. This study aims to address these challenges by exploring the use of lignocellulosic agricultural residues as feedstocks for microbial protein fermentation, focusing on Fusarium venenatum A3/5, a mycelial strain known for its high protein yield and nutritional quality. We propose a high throughput microlitre batch fermentation system paired with analytical chemistry to generate time series data of microbial growth and substrate utilisation. An unstructured biokinetic model was developed using a bootstrap sampling approach to quantify uncertainty in the parameter estimates. The model was validated against an independent data set of a different glucose-xylose composition to assess the predictive performance. Our results indicate a robust model fit with high coefficients of determination and low root mean squared errors for biomass, glucose, and xylose concentrations. Estimated parameter values provided insights into the resource utilisation strategies of Fusarium venenatum A3/5 in mixed substrate cultures, aligning well with previous research findings. Significant correlations between estimated parameters were observed, highlighting challenges in parameter identifiability. The high throughput workflow presents a novel, rapid methodology for biokinetic model development, enabling efficient exploration of microbial growth dynamics and substrate utilisation. This innovative method directly supports the development of a foundational model for optimising microbial protein production from lignocellulosic hydrolysates, contributing to a more sustainable global food system.
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来源期刊
Current Research in Food Science
Current Research in Food Science Agricultural and Biological Sciences-Food Science
CiteScore
7.40
自引率
3.20%
发文量
232
审稿时长
84 days
期刊介绍: Current Research in Food Science is an international peer-reviewed journal dedicated to advancing the breadth of knowledge in the field of food science. It serves as a platform for publishing original research articles and short communications that encompass a wide array of topics, including food chemistry, physics, microbiology, nutrition, nutraceuticals, process and package engineering, materials science, food sustainability, and food security. By covering these diverse areas, the journal aims to provide a comprehensive source of the latest scientific findings and technological advancements that are shaping the future of the food industry. The journal's scope is designed to address the multidisciplinary nature of food science, reflecting its commitment to promoting innovation and ensuring the safety and quality of the food supply.
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