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相关概念视频

Fermentation01:29

Fermentation

128.8K
Most eukaryotic organisms require oxygen to survive and function adequately. Such organisms produce large amounts of energy during aerobic respiration by metabolizing glucose and oxygen into carbon dioxide and water. However, most eukaryotes can generate some energy in the absence of oxygen by anaerobic metabolism.
Fermentation is a type of metabolic process that occurs in the absence of oxygen, where organic molecules such as glucose are broken down to produce energy. During this process, the...
128.8K

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相关实验视频

Updated: Jan 18, 2026

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth
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用压力引导的LSTM建模用于发酵量化预测.

Jooho Lee1, Jieun Jeong1, Sangoh Kim1

  • 1Department of Food Engineering, Dankook University, 119 Dandae-ro, Dongnam-gu, Cheonan-si 31116, Chungcheongnam-do, Republic of Korea.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
概括
此摘要是机器生成的。

这项研究将AI预测模型与区块链集成在一起,以可靠地监测发酵. 长短期记忆 (LSTM) 模型准确预测发酵动态,增强生物过程控制.

关键词:
这是LSTM的LSTM.人工智能的人工智能是人工智能.区块链区块链区块链区块链区块链发酵 发酵 发酵 发酵物联网的东西互联网.

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Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
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Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

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相关实验视频

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科学领域:

  • 生物技术和生物化学工程 生物技术和生物化学工程
  • 数据科学和人工智能数据科学和人工智能

背景情况:

  • 由于复杂的,非线性环境变量,实时发酵监测面临着挑战.
  • 现有的传感器技术与生物过程的动态性质作斗争.

研究的目的:

  • 制定一个综合框架,提高发酵监测的可靠性和透明度.
  • 利用深度学习和区块链来准确预测发酵动态.

主要方法:

  • 开发了一种基于长短期记忆 (LSTM) 的发酵过程预测模型 (FPPM).
  • 使用模块化传感器单元 (PBSU,GBSU,FQSU) 进行多变量时间序列数据采集.
  • 实施了发酵区块链云系统 (FBCS) 以确保数据日志和完整性.

主要成果:

  • 对于发酵百分比 (FP) 和发酵量化 (FQ),LSTM模型实现了高预测精度,R2值从0.8547到0.9437.
  • 估计的FQ值与实际测量有很强的一致性.
  • 综合框架通过区块链技术确保了数据完整性和可追溯性.

结论:

  • 这项研究证明了人工智能驱动的预测模型对生物过程控制的可行性.
  • 人工智能与去中心化数据基础设施的整合提供了强大的和可扩展的发酵监控.
  • 拟议的框架提高了发酵过程管理的可靠性和透明度.