Rapid identification of fermentation stages of bioethanol solid-state fermentation (SSF) using FT-NIR spectroscopy: comparisons of linear and non-linear algorithms for multiple classification issues

Abstract

Solid-state fermentation (SSF) is a critical step in bioethanol production, and a means for the effective monitoring of the SSF process is urgently needed due to the rapid changes in the SSF industry, which demands fast tools that could provide real time information to ensure the quality of the final product. The aim of the present study was to investigate the FT-NIR spectroscopy technique associated with supervised pattern recognition methods in order to develop a means to monitor the time-related molecular changes that occur during the SSF of bioethanol. Principal component analysis as an exploratory tool was employed to uncover details on the molecular modifications of the spectral data during the SSF process. Furthermore, identification models were constructed using partial least squares discriminant analysis (PLS-DA), back propagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM) algorithms. The parameters of the four algorithms were optimized by leave-one-out cross-validation (LOOCV) for the calibration of the identification models. The experimental results showed that the nonlinear identification models achieved strong classification performance to identify the fermentation stages in the SSF of bioethanol. Moreover, compared with the BPNN and SVM models, the ELM model achieved a slightly better generalization performance with an identification rate of 92.60% in the validation process. The overall results show that the ELM-FT-NIR methodology was efficient in accurately identifying the fermentation stages during the SSF of bioethanol, thus demonstrating its potential for application in the in situ monitoring and control of large-scale industrial processes.