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Related Concept Videos

Microbes in Food Production01:29

Microbes in Food Production

Microbial fermentation is central to food biotechnology, enhancing flavor, texture, preservation, and stability. Fermentative microorganisms metabolize carbohydrates into organic acids, alcohols, and other metabolites that inhibit spoilage organisms and improve digestibility while contributing distinctive sensory qualities.In baking, amylases naturally present in flour hydrolyze starch into monosaccharides such as glucose, which Saccharomyces cerevisiae ferments anaerobically. Through...
Microbes in Beverage Production01:25

Microbes in Beverage Production

Alcoholic beverages such as wine, beer, and spirits are the products of microbial fermentation processes that transform simple sugars into ethanol and a wide array of complex flavor compounds. These transformations rely on the metabolic activities of specific yeasts and bacteria, which are selected and controlled to yield the desired beverage characteristics.Wine Fermentation and MaturationWine production begins with the crushing of grapes to release juice and pulp, forming a must that is...
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Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
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The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
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Continuous fermentation is a key strategy in industrial ethanol production, particularly when efficiency, scalability, and high yields are essential. This approach allows for uninterrupted operation and optimized resource utilization. The primary feedstock, corn starch, undergoes enzymatic hydrolysis facilitated by α-amylase and glucoamylase. These enzymes break down the starch into fermentable sugars such as glucose, which are readily assimilated by fermentative microorganisms.Fermentation...

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Application of machine learning for quantitative analysis of industrial fermentation using image processing.

Jieun Jeong1, Sangoh Kim1

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

Food Science and Biotechnology
|February 13, 2025
PubMed
Summary

A new Real-time Fermentation Quantification Sensor (RFQS) uses AI image analysis to monitor fermentation progress. This technology accurately measures fermentation degree, offering a valuable tool for the food industry.

Keywords:
AIFermentationFermentation quantification sensorMachine learningVision system

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Area of Science:

  • Food Science and Technology
  • Biotechnology
  • Artificial Intelligence in Manufacturing

Background:

  • Traditional fermentation monitoring relies on indirect or time-consuming methods.
  • Accurate, real-time quantification of fermentation is crucial for process optimization and quality control.
  • The need for automated, non-invasive monitoring systems in the food industry is increasing.

Purpose of the Study:

  • To develop and validate a Real-time Fermentation Quantification Sensor (RFQS) for quantitative fermentation assessment.
  • To integrate a Convolutional Neural Network (CNN) model for image analysis of fermentation byproducts.
  • To establish a real-time fermentation degree measurement system using AI-powered image processing.

Main Methods:

  • Development of the RFQS to detect and capture images of airlock bubbles generated by fermentation gas.
  • Integration of a CNN-based Fermentation Measurement Model for analyzing bubble images.
  • Conducting validation experiments with varying yeast and glucose concentrations to assess fermentation dynamics.
  • Calculating total fermentation degree using real-time sensor data upon completion.

Main Results:

  • The RFQS successfully monitored fermentation by analyzing airlock bubble images.
  • Varying yeast and glucose quantities were shown to significantly affect fermentation duration and degree.
  • The integrated CNN model accurately classified bubble images for continuous monitoring.
  • Real-time data allowed for the calculation of total fermentation degree post-completion.

Conclusions:

  • AI-based image processing, via the RFQS, provides an effective quantitative measurement tool for fermentation.
  • The developed system enables continuous, real-time monitoring of fermentation degree.
  • This technology holds significant potential for enhancing process control and efficiency in the fermentation food industry.