Comparison of partial least square, artificial neural network, and support vector regressions for real‐time monitoring of CHO cell culture processes using in situ near‐infrared spectroscopy-Reference-Cited by-同舟云学术

Comparison of partial least square, artificial neural network, and support vector regressions for real‐time monitoring of CHO cell culture processes using in situ near‐infrared spectroscopy

Published:2021-12-06 Issue:2 Volume:119 Page:535-549
ISSN:0006-3592
Container-title:Biotechnology and Bioengineering
language:en
Short-container-title:Biotech & Bioengineering

Author:

Zavala‐Ortiz Daniel A.¹²^ORCID,Denner Aurélia¹,Aguilar‐Uscanga Maria G.²^ORCID,Marc Annie¹,Ebel Bruno¹^ORCID,Guedon Emmanuel¹^ORCID

Affiliation:

1. Laboratoire Réactions et Génie des Procédés Université de Lorraine, CNRS, LRGP Vandœuvre‐lès‐Nancy France

2. Tecnológico Nacional de México/Instituto Tecnológico de Veracruz Veracruz, Ver. México

Publisher

Wiley

Subject

Applied Microbiology and Biotechnology,Bioengineering,Biotechnology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/bit.27997

Reference44 articles.

1. Fermi Resonance Nonlinear Waves and Solitons in Organic Superlattices

2. A comparative study of K-Nearest Neighbour, Support Vector Machine and Multi-Layer Perceptron for Thalassemia screening

3. Graphs in Statistical Analysis

4. In-situ near infrared spectroscopy to monitor key analytes in mammalian cell cultivation

5. Support vector machine regression (SVR/LS-SVM)—an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing cross‐scale Raman in‐line monitoring capability of cell culture process in large‐scale manufacturing;AIChE Journal;2024-09-09

2. A neural ordinary differential equation model for predicting the growth of Chinese Hamster Ovary cell in a bioreactor system;Biotechnology and Bioprocess Engineering;2024-09-01

3. Machine learning algorithms for in-line monitoring during yeast fermentations based on Raman spectroscopy;Vibrational Spectroscopy;2024-05

4. Convolutional Neural Networks and Raman Spectroscopy for Semi-Supervised Dataset Construction and Transfer Learning Applications in Real-Time Quantitative Detection;Proceedings of the 2024 4th International Conference on Bioinformatics and Intelligent Computing;2024-01-26

5. Genetic algorithm‐based semisupervised convolutional neural network for real‐time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor;Biotechnology and Bioengineering;2024-01-22