Research on Batch Classification of γ-Polyglutamic-Acid Fermentation Based on ATR-FTIR Spectroscopy

Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) technology is utilized to quickly identify and detect different fermentation batches for γpolyglutamic-acid(γ-PGA). Based on partial least squares discriminant analysis(PLSDA), five classification models were established to distinguish each batch from other batches, which performed well on several individual evaluation indicators(e. g., accuracy). In order to improve the model performance on all the indicators(e. g., accuracy, precision, sensitivity, etc.)and the model interpretability, three wavenumber selection methods including subwindow permutation analysis(SPA), competitive adaptive reweighted sampling(CARS) and random frog(RF) were combined with PLSDA to extract key wavenumbers and then established the corresponding classification models. Experimental results show that all the performance indicators of PLSDA combined with wavenumber selection(except CARS-PLSDA) are improved. Additionally, both the model complexity and interpretability are improved. Therefore, ATR-FTIR technology combined with SPA-PLSDA or RF-PLSDA method can realize rapid identification of different γ-PGA fermentation batches. © 2022 Northeastern University. All rights reserved.