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Related Experiment Video

Updated: Jul 4, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Maximum likelihood estimation of growth yields.

B O Solomon1, L E Erickson, J E Hess

  • 1Department of Chemical Engineering, Kansas State University, Manhattan, Kansas 66506.

Biotechnology and Bioengineering
|March 1, 1982
PubMed
Summary
This summary is machine-generated.

This study introduces a statistical method to estimate microbial growth parameters. The maximum likelihood estimator combines multiple measurements for more accurate yield and maintenance estimations.

Related Experiment Videos

Last Updated: Jul 4, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Biotechnology
  • Microbial Physiology
  • Statistical Modeling

Background:

  • Microbial growth is crucial in biotechnology and requires accurate estimation of yield and maintenance parameters.
  • Traditional methods often analyze correlated variables independently, potentially losing information.
  • Accurate parameter estimation is vital for optimizing bioprocesses.

Purpose of the Study:

  • To develop and evaluate a statistical method for estimating microbial yield and maintenance parameters.
  • To integrate information from correlated response variables for improved parameter estimation.
  • To provide both point and interval estimators for practical application.

Main Methods:

  • Utilized a maximum likelihood estimator (MLE) approach.
  • Combined correlated response variables: substrate concentration, oxygen utilization rate, carbon dioxide evolution rate, and biomass concentration.
  • Evaluated the proposed method using simulated and literature data.

Main Results:

  • The proposed maximum likelihood estimator effectively integrates information from multiple correlated measurements.
  • The method provides accurate point and interval estimates for yield and maintenance parameters.
  • Validation against literature data confirms the estimator's reliability.

Conclusions:

  • The developed statistical method offers a robust approach for estimating key microbial growth parameters.
  • Integrating correlated data enhances the precision of yield and maintenance parameter estimations.
  • This method can improve the efficiency and accuracy of bioprocess modeling and optimization.