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Fermentation

Fermentation

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...

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Related Experiment Video

Updated: Jan 18, 2026

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Published on: July 3, 2017

Pressure-Guided LSTM Modeling for Fermentation Quantification Prediction.

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

Summary

This summary is machine-generated.

This study integrates AI prediction models with blockchain for reliable fermentation monitoring. The Long Short-Term Memory (LSTM) model accurately predicts fermentation dynamics, enhancing bioprocess control.

Keywords:

LSTM artificial intelligence blockchain fermentation internet of things

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Last Updated: Jan 18, 2026

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

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Published on: July 3, 2017

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

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

Published on: January 22, 2018

Area of Science:

Biotechnology and Biochemical Engineering
Data Science and Artificial Intelligence

Background:

Real-time fermentation monitoring faces challenges due to complex, nonlinear environmental variables.
Existing sensor technologies struggle with the dynamic nature of bioprocesses.

Purpose of the Study:

To develop an integrated framework for enhanced reliability and transparency in fermentation monitoring.
To leverage deep learning and blockchain for accurate prediction of fermentation dynamics.

Main Methods:

Developed a Long Short-Term Memory (LSTM)-based Fermentation Process Prediction Model (FPPM).
Utilized modular sensor units (PBSU, GBSU, FQSU) for multivariate time-series data acquisition.
Implemented a Fermentation-Blockchain-Cloud System (FBCS) for secure data logging and integrity.

Main Results:

LSTM models achieved high predictive accuracy for Fermentation Percent (FP) and Fermentation Quantification (FQ), with R² values from 0.8547 to 0.9437.
Estimated FQ values demonstrated strong concordance with actual measurements.
The integrated framework ensured data integrity and traceability through blockchain technology.

Conclusions:

The study demonstrates the feasibility of AI-driven prediction models for bioprocess control.
Integration of AI with decentralized data infrastructure offers robust and scalable fermentation monitoring.
The proposed framework enhances the reliability and transparency of fermentation process management.