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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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[Determination of process variable pH in solid-state fermentation by FT-NIR spectroscopy and extreme learning machine

Guo-hai Liu1, Hui Jiang, Xia-hong Xiao

  • 1School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China. ghliu@ujs.edu.cn

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|June 22, 2012
PubMed
Summary

Fourier transform near-infrared (FT-NIR) spectroscopy accurately determined pH in solid-state fermentation using extreme learning machine (ELM) calibration. This method offers a reliable basis for real-time monitoring of fermentation processes.

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Area of Science:

  • Agricultural Science
  • Analytical Chemistry
  • Biotechnology

Context:

  • Solid-state fermentation (SSF) of crop straws is crucial for biofuel and biochemical production.
  • Accurate pH monitoring is essential for optimizing SSF processes and product yield.
  • Traditional pH measurement methods can be time-consuming and labor-intensive.

Purpose:

  • To develop and validate a Fourier transform near-infrared (FT-NIR) spectroscopy method for determining pH in SSF.
  • To calibrate a model using extreme learning machine (ELM) for accurate pH prediction.
  • To establish a basis for on-line pH measurement during SSF.

Summary:

  • Near-infrared spectra (10,000–4,000 cm⁻¹) of 140 SSF crop straw samples were collected.
  • An extreme learning machine (ELM) model was calibrated using cross-validation to determine optimal parameters.
  • The optimal ELM model (1040-1 hidden layer nodes) achieved high prediction accuracy (R(p) = 0.9618, RMSEP = 0.1044).

Impact:

  • Provides a rapid and non-destructive method for pH determination in SSF.
  • Enables real-time process monitoring and control, potentially improving fermentation efficiency.
  • Offers a technological foundation for developing advanced on-line analytical tools for bioprocesses.