Semi-Supervised Learning Based Calibration Modeling of Near-Infrared Spectroscopy for Monitoring 1,3-Propanediol Fermentation Process Components

Concerning the real-time monitoring of important component contents during a fermentation process of 1,3-propanediol, a near-infrared (NIR) spectral calibration modeling method is proposed based on a semi-supervised learning strategy. To solve the problems of high dimensionality and serious information overlap of NIR spectra, a binary mayfly optimization algorithm (BMA) is proposed to select the characteristic wavenumbers of the measured spectra for model calibration. To address the issue of insufficient labeled sample dataset, a robust semi-supervised probabilistic principal component regression (RSSPPCR) modeling method is presented based on the expectation maximization (EM) algorithm, which could fully utilize all labeled and unlabeled spectral data. The student t-distribution is introduced into the modeling to improve the model tolerance of outliers, so as to ensure the reliability of simultaneous detection of multiple components during the fermentation process. The experimental results show that the proposed model can effectively predict the important contents of glycerol (substrate), 1,3-propanediol (product), and biomass during a fermentation process of 1,3-propanediol.