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相关概念视频

Microbial Fermentation01:23

Microbial Fermentation

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Fermentation is a crucial anaerobic metabolic process that enables microbes to derive energy from sugar without relying on oxygen or an electron transport chain. This process is fundamental to various biological and industrial applications and is classified based on the metabolic products generated.Role of Pyruvate in FermentationPyruvate and its derivatives serve as key electron acceptors in fermentative pathways. The oxidation of NADH to regenerate NAD+ is essential for the continuation of...
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Updated: Sep 19, 2025

Techniques for the Evolution of Robust Pentose-fermenting Yeast for Bioconversion of Lignocellulose to Ethanol
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数据增强的深度学习算法用于准确控制生物乙醇发酵,使用在线拉曼分析仪.

Kaidi Ji1, Xiaofei Yu2, Lifan Chen2

  • 1School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, P.R. China.

Biotechnology and bioengineering
|June 9, 2025
PubMed
概括

这项研究引入了拉曼光谱和深度学习系统,以优化料批发发酵. 新方法通过精确控制葡萄糖养来提高生物乙醇生产,从而提高产量和减少副产品.

关键词:
数据增强数据增强深度学习是一种深度学习.反控制反的控制方法发酵 发酵 发酵 发酵拉曼分析仪 拉曼分析仪

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科学领域:

  • 生物技术是生物技术.
  • 化学工程是化学工程的重要组成部分.
  • 频谱学是一种光谱学.

背景情况:

  • 料批发发酵对于工业生物制造至关重要,但优化料策略以获得最大产量仍然具有挑战性.
  • 精确的实时监测关键代谢物如葡萄糖对于有效的过程控制至关重要.
  • 有限的标记数据往往阻碍了对生物过程的先进机器学习模型的开发.

研究的目的:

  • 开发和验证基于拉曼光谱的在线监测和控制系统,以优化料批发发酵.
  • 通过半监督学习和一种新的深度学习架构来提高葡萄糖预测的准确性.
  • 通过自动葡萄糖养来证明增强生物乙醇生产和减少糖醇形成.

主要方法:

  • 实施在线拉曼光谱系统,用于实时监测生物过程.
  • 应用伪标签方法来扩大有限的标签数据用于半监督学习.
  • 开发一个光谱-时间连接卷积神经网络 (STC-CNN) 来分析连续的光谱特征.
  • 基于实时葡萄糖预测的自动葡萄糖养控制.

主要成果:

  • 该STC-CNN模型实现了低根平均平方误差 (RMSE) 为3.63g/L的葡萄糖预测,优于其他机器学习算法.
  • 开发的系统使得快速和自动的葡萄糖养能够保持目标度.
  • 葡萄糖设定值为30g/L,导致乙醇度最高 (140.68g/L),比传统方法增加3.85%.
  • 在优化养策略下,糖醇产量减少了6.67%.

结论:

  • 拉曼光谱与深度学习相结合,为自动化生物过程监测和控制提供了一种强大的方法.
  • 开发的STC-CNN模型和伪标签策略有效地解决了生物过程优化中的数据限制.
  • 这种综合系统显著提高了Saccharomyces cerevisiae发酵中的生物乙醇生产效率和可持续性.