[State Recognition of Solid Fermentation Process Based on Near Infrared Spectroscopy with Adaboost and Spectral...

Area of Science:

• Analytical Chemistry
• Biotechnology
• Spectroscopy

Background:

• Solid-state fermentation (SSF) requires efficient monitoring for process optimization.
• Traditional monitoring methods can be time-consuming and destructive.
• Fourier transform near infrared (FT-NIR) spectroscopy offers a non-invasive approach for analyzing biological processes.

Purpose of the Study:

• To develop a rapid and accurate method for qualitative identification of the process state in feed protein SSF.
• To utilize FT-NIR spectroscopy combined with advanced algorithms for real-time monitoring.
• To establish a non-destructive analytical tool for SSF process control.

Main Methods:

• Collected FT-NIR spectra from 140 feed protein SSF samples.
• Preprocessed spectra using Standard Normal Variate (SNV) transformation.
• Extracted spectral features using Spectral Regression Discriminant Analysis (SRDA).
• Developed classification models using Nearest Neighbors (NN) algorithm, including SRDA-NN, PCA-NN, and LDA-NN.
• Proposed and implemented an Adaboost-SRDA-NN ensemble learning algorithm to enhance recognition accuracy.

Main Results:

• The SRDA-NN model achieved a 94.28% correct recognition rate, outperforming PCA-NN and LDA-NN models.
• The Adaboost-SRDA-NN ensemble model further improved performance, reaching a 100% correct recognition rate in the validation set.
• SRDA effectively performed spectral feature extraction and dimension reduction for qualitative NIR analysis.
• Adaboost algorithm significantly enhanced the classification accuracy of the final model.

Conclusions:

• FT-NIR spectroscopy combined with SRDA and Adaboost-SRDA-NN algorithms provides a highly effective tool for rapid and accurate online monitoring of SSF processes.
• The developed method eliminates the need for traditional chemical analysis, offering a non-destructive and efficient alternative.
• This research lays the groundwork for developing practical online monitoring instruments for SSF applications.