Abstract
One of the main challenges in ferentation cultures is the monitoring of key variables that can indicate the performance and help in the optimization of the bioprocess. On-line estimation could be a challenging task when an accurate process model is not available. Support Vector Machine (SVM) is an attractive and relatively simple method that can be used as an alternative to predict key variables by using several physical parameters measured online. In this paper, we show the application of SVM to the production of protein by B. thuringiensis. The soft-sensor was trained and validated with independent data sets of batch fermentations evaluating the impact of different predictor variables and kernels. Results show that protein production can be predicted only using online measurements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schnepf, E.: Bacillus thuringiensis and its pesticidal crystal proteins. Microbiol. Mol. Biol. Rev. 62(3), 775–806 (1998). Chen, W.-K.: Linear Networks and Systems (Book style), pp. 123–135. Wadsworth, Belmont (1993)
Iriarte, J., Porcar, M., Lecadet, M.-M., Caballero, P.: Isolation and characterization of bacillus thuringiensis strains from aquatic environments in Spain. Curr. Microbiol. 40, 402–408 (2000). Smith, B.: An approach to graphs of linear forms (Unpublished work style). unpublished
Rowe, G.E., Margaritis, A.: Bioprocess design and economic analysis for the commercial production of environmentally friendly bioinsecticides from bacillus thuringiensis HD-1 kurstaki. Biotechnol. Bioeng. 86(4) (2004)
El Khoury, M., Azzouz, H., Chavanieu, A., Abdelmalak, N., Chopineau, J., Awad, M.K.: Isolation and characterization of a new Bacillus thuringiensis strain Lip harboring a new cry1Aa gene highly toxic to Ephestia kuehniella (Lepidoptera: Pyralidae) larvae. Arch. Microbiol. 196(6), 435–444 (2014)
Vapnik, V., Golowich, S.E., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing. Annu. Conf. Neural Inf. Process. Syst. 281–287 (1996)
Liu, G., Zhou, D., Xu, H., Mei, C.: Model optimization of SVM for a fermentation soft sensor. Expert Syst. Appl. 37, 2708–2713 (2010)
Ou Yang, H.-B., Li, S., Zhang, P., Kong, X.: Model penicillin fermentation by least squares support vector machine with tuning based on amended harmony search. Int. J. Biomath. 08, 1550037 (2015)
Ahmad, M.W., Reynolds, J., Rezgui, Y.: Predictive modelling for solar thermal energy systems: a comparison of support vector regression, random forest, extra trees and regression trees. J. Clean. Prod. 203, 810–821 (2018)
Zhu, X., Rehman, K.U., Wang, B., Shahzad, M.: Modern soft-sensing modeling methods for fermentation processes. Sensors(Switzerland), 20(6), 1771 (2020).
Jianlin, W., Tao, Y.U., Cuiyun, J.I.N.: On-line estimation of biomass in fermentation process using support vector machine. Chinese J. Chem. Eng. 14, 383–388 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Robles Rodriguez, C.E. et al. (2022). Soft-Sensors for Monitoring B. Thuringiensis Bioproduction. In: Matsui, K., Omatu, S., Yigitcanlar, T., González, S.R. (eds) Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-86261-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-86261-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86260-2
Online ISBN: 978-3-030-86261-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)