γ-polyglutamic acid fermentation monitoring with ATR-FTIR spectroscopy based on a shallow convolutional neural network combined with data augmentation and attention module

Abstract

The potential of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to online real-time monitor the γ-polyglutamic acid (γ-PGA) fermentation process by measuring the concentration of two key components, glucose and sodium glutamate, was investigated. Partial least squares regression (PLSR) and convolutional neural network (CNN) were selected as the multivariate calibration model to predict fermentation parameters. To solve the small sample training problem with CNN, the data augmentation strategy was firstly combined with a shallow CNN to generate a new CNN model termed as DA-CNN. To improve the performance of DA-CNN further, squeeze-and-excitation (SE) module of the attention mechanism was integrated into DA-CNN to form a DA-CNN variant termed as DA-SE-CNN, which could capture the different importance of extracted channel-wise feature to improve feature representation ability. Experiments were conducted on the γ-PGA dataset, and the results showed that the nonlinear models (CNN, DA-CNN and DA-SE-CNN) performed better than the linear method (PLSR) in all cases except the case of sodium glutamate prediction with CNN. Additionally, both DA-CNN and DA-SE-CNN perform better than CNN in all cases with limited training data. Especially DA-SE-CNN gave the best result with excellent prediction accuracy, which indicated that ATR-FTIR combined with nonlinear regression tool (DA-SE-CNN) as a rapid method to monitor γ-PGA fermentation process is feasible.