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Related Concept Videos

Fermentation01:29

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...
Microbial Fermentation01:23

Microbial Fermentation

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...
Production of Alcohol01:27

Production of Alcohol

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...
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...
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...
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...

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Profiling the Bacterial Community of Fermenting Traminette Grapes during Wine Production using Metagenomic Amplicon Sequencing
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Fermentation database mining by pattern recognition.

G Stephanopoulos1, G Locher, M J Duff

  • 1Department of Chemical Engineering, Room 66-552, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Biotechnology and Bioengineering
|March 5, 1997
PubMed
Summary
This summary is machine-generated.

This study introduces a novel methodology for analyzing fermentation data, enabling automated pattern recognition and interpretation. The developed software, db-miner, efficiently identifies key process variables linked to specific fermentation outcomes.

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Area of Science:

  • Biotechnology
  • Process Engineering
  • Data Science

Background:

  • Industrial fermentation processes generate large volumes of data, essential for documentation.
  • Current data analysis often has low information density, hindering process improvement.
  • Automated data condensing, analysis, and interpretation are highly desirable for optimizing processes.

Purpose of the Study:

  • To present a methodology for processing process variables into derivative quantities.
  • To develop a search algorithm for extracting unique process variables characterizing specific outcomes.
  • To demonstrate the application of this methodology in industrial fermentation data analysis.

Main Methods:

  • Developed a pattern recognition methodology to create a database of derivative process quantities.
  • Utilized a powerful search algorithm to identify variables linked to process outcomes (e.g., high/low yield).
  • Implemented the methodology in user-friendly software named db-miner for efficient data analysis.

Main Results:

  • The methodology successfully processed industrial fermentation data, identifying global patterns, intermediate trends, and local characteristics.
  • Discriminating variables crucial for understanding different process outcomes were identified.
  • Results indicate the potential for identifying causes or symptoms of varied fermentation performance.

Conclusions:

  • The presented methodology effectively extracts meaningful information from complex fermentation datasets.
  • The db-miner software facilitates rapid and efficient analysis of fermentation process data.
  • This approach aids in identifying critical process variables for improved fermentation control and yield optimization.