Online quantitative substrate, product, and cell concentration in citric acid fermentation using near-infrared...

As a mature platform compound, citric acid (CA) is mainly produced by Aspergillus niger (A. niger) through submerged fermentation. However, the CA fermentation process is still regulated based on experience and limited offline data, so real-time monitoring and intelligent precise control of the fermentation process cannot be carried out. In this study, near-infrared (NIR) spectroscopy combined with different chemometrics methods was used to quantify the substrate, product, and cell concentration of CA fermentation online. The predictive performance of total sugar (TS), CA, and dry cell weight (DCW) concentrations were compared between traditional partial least squares (PLS) and intelligent stacked auto-encoder (SAE) modeling methods. Theresults showed that both PLS and SAE models had good performance in predicting TS and CA. The performance, accuracy, and precision of the PLS models are slightly better than those of the SAE models in predicting TS and CA. SAE model was superior to the PLS model in predicting DCW concentration. The SAE modeling method has advantages in predicting the concentration of complex components. In this study, the multi-parameter online prediction was realized in the complex system of CA fermentation, which provided the basis for real-time intelligent control of the fermentation process.