Correlation Engine 2.0
Clear Search sequence regions

Sizes of these terms reflect their relevance to your search.

Ectoine is generally produced by the fermentation process of Halomonas elongata DSM 2581 T, which is one of the primary industrial ectoine production techniques. To effectively monitor and control the fermentation process, the important parameters require accurate real-time measurement. However, for ectoine fermentation, three critical parameters (cell optical density, glucose, and product concentration) cannot be measured conveniently in real-time due to time variation, strong coupling, and other constraints. As a result, our work effectively created a series of hybrid models to predict the values of these three parameters incorporating both fermentation kinetics and machine learning approaches. Compared with the traditional machine learning models, our models solve the problem of insufficient data which is common in fermentation. In addition, a simple kinetic modeling is only applicable to specific physical conditions, so different physical conditions require refitting the function, which is tedious to operate. However, our models also overcome this limitation. In this work, we compared different hybrid models based on 5 feature engineering methods, 11 machine-learning approaches, and 2 kinetic models. The best models for predicting three key parameters, respectively, are as follows: CORR-Ensemble (R2: 0.983 ± 0.0, RMSE: 0.086 ± 0.0, MAE: 0.07 ± 0.0), SBE-Ensemble (R2: 0.972 ± 0.0, RMSE: 0.127 ± 0.0, MAE: 0.078 ± 0.0), and SBE-Ensemble (R2:0.98 ± 0.0, RMSE: 0.023 ± 0.001, MAE: 0.018 ± 0.001). To verify the universality and stability of constructed models, we have done an experimental verification, and its results showed that our proposed models have excellent performance. KEY POINTS: • Using the kinetic models for producing simulated data • Through different feature engineering methods for dimension reduction • Creating a series of hybrid models to predict the values of three parameters in the fermentation process of Halomonas elongata DSM 2581 T. © 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.


Guanxue Lai, Junxiong Yu, Jing Wang, Weihua Li, Guixia Liu, Zejian Wang, Meijin Guo, Yun Tang. Machine learning methods for predicting the key metabolic parameters of Halomonas elongata DSM 2581 T. Applied microbiology and biotechnology. 2023 Sep;107(17):5351-5365

Expand section icon Mesh Tags

Expand section icon Substances

PMID: 37421474

View Full Text