Rapid Diagnosis of Normal and Abnormal Conditions in Solid-State Fermentation of Bioethanol Using Fourier Transform Near-Infrared Spectroscopy

Abstract

Rapid diagnosis of normal and abnormal fermentation conditions in solid-state fermentation (SSF) of bioethanol is crucial to process and quality controls. The Fourier transform near-infrared (FT-NIR) spectroscopy analysis technique combined with pattern recognition methods was employed to monitor fermentation conditions (i.e., normal and abnormal) in this study. To achieve optimum performance in identification of fermentation conditions, linear discriminant analysis (LDA), K-nearest neighbors (KNN), and support vector machine (SVM) classifications were employed to construct the recognition models. The optimization of arithmetical parameters and number of principal components (PCs) were implemented simultaneously using leave-one-out cross-validation (LOOCV) during the recognition model training phase. The results of this study revealed that the SVM model presented better performance compared to the other two models, and the best SVM recognition model was finally established by use of four PCs. The SVM mode...