Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy...

Area of Science:

• Agricultural Science
• Analytical Chemistry
• Biotechnology

Background:

• Solid-state fermentation (SSF) is crucial for agricultural waste utilization.
• Accurate monitoring of process variables like pH and moisture is essential for optimizing SSF.
• Traditional methods for monitoring SSF are often time-consuming and labor-intensive.

Purpose of the Study:

• To investigate the feasibility of using Fourier transform near infrared (FT-NIR) spectroscopy for rapid determination of pH and moisture content in wheat straw SSF.
• To develop and optimize a robust chemometric model for accurate prediction of these parameters.
• To evaluate the performance of the synergy interval partial least squares (siPLS) algorithm compared to other methods.

Main Methods:

• Fourier transform near infrared (FT-NIR) spectroscopy was employed to collect spectral data from wheat straw during SSF.
• A synergy interval partial least squares (siPLS) algorithm was used for model calibration, optimizing factors and subintervals via cross-validation.
• Model performance was assessed using RMSECV, RMSEP, and correlation coefficients (R).

Main Results:

• The optimized FT-NIR and siPLS model achieved high accuracy for pH prediction (R(c)=0.9777, R(p)=0.9686) and moisture content prediction (R(c)=0.8871, R(p)=0.8684).
• The siPLS model demonstrated superior performance compared to conventional partial least squares (PLS) and interval PLS (iPLS) models.
• Low RMSECV and RMSEP values indicated reliable prediction capabilities for both parameters.

Conclusions:

• FT-NIR spectroscopy, coupled with the siPLS algorithm, provides a rapid and accurate method for monitoring key process variables in wheat straw SSF.
• This spectroscopic technique holds significant potential for real-time process control and optimization within the SSF industry.
• The developed method offers a non-destructive and efficient alternative to traditional analytical techniques for SSF monitoring.