Comparison of chemometric models using Raman spectroscopy for offline biochemical monitoring throughout the VLP-making upstream process
This work aimed to compare the predictive capacity of linear (Partial Least Squares; Principal Component Regression) and non-linear (Artificial Neural Network, ANN; Support Vector Machine, SVM) chemometric models from Raman spectroscopy spectra acquired offline with (unclarified samples) and without...
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Published in | Biochemical engineering journal Vol. 198; p. 109013 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.09.2023
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Abstract | This work aimed to compare the predictive capacity of linear (Partial Least Squares; Principal Component Regression) and non-linear (Artificial Neural Network, ANN; Support Vector Machine, SVM) chemometric models from Raman spectroscopy spectra acquired offline with (unclarified samples) and without cells (clarified samples), and biochemical parameters (Viable cell density, cell viability, glucose, lactate, glutamine, glutamate, ammonium, and potassium) quantified by reference methods in a rabies VLP production bioprocess. The nonlinear models, ANN and SVM were the more suitable models with the lowest absolute errors. No effective differences in accuracy were detected for models from spectral data with and without cells. The mean absolute error of the best models within the assessed parameter ranges for viable cell density (0.01–8.83 × 106 cells/mL), cell viability (1.3–100.0%), glucose (5.22–10.93 g L−1), lactate (18.6–152.7 mg L−1), glutamine (158–1761 mg L−1), glutamate (807.6–2159.7 mg L−1), ammonium (62.8–117.8 mg L−1), and potassium (531.6–685. 3 mg L−1) were 0.17 ± 0.13 × 106 cells/mL, 6.8 ± 7.1%, 0.24 ± 0.14 g L− 1, 3.3 ± 3.5 mg L− 1, 61 ± 52 mg L− 1, 52 ± 51 mg L− 1, 2.1 ± 1.9 mg L− 1 and 8.6 ± 8.6 mg L− 1, respectively. The errors achieved were like those for bioprocesses using a highlighted animal cell host (CHO cell) in similar ranges.
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•Monitoring of Rabies virus-like particles production using offline Raman spectra.•Accuracy was alike between chemometrics models for samples with and without cells.•Nonlinear models showed a better fit than linear ones in biochemical monitoring.•The fitted models can be used in high-throughput upstream process development.•Variables absolute error was like those for other host cells covering alike ranges. |
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AbstractList | This work aimed to compare the predictive capacity of linear (Partial Least Squares; Principal Component Regression) and non-linear (Artificial Neural Network, ANN; Support Vector Machine, SVM) chemometric models from Raman spectroscopy spectra acquired offline with (unclarified samples) and without cells (clarified samples), and biochemical parameters (Viable cell density, cell viability, glucose, lactate, glutamine, glutamate, ammonium, and potassium) quantified by reference methods in a rabies VLP production bioprocess. The nonlinear models, ANN and SVM were the more suitable models with the lowest absolute errors. No effective differences in accuracy were detected for models from spectral data with and without cells. The mean absolute error of the best models within the assessed parameter ranges for viable cell density (0.01–8.83 × 106 cells/mL), cell viability (1.3–100.0%), glucose (5.22–10.93 g L−1), lactate (18.6–152.7 mg L−1), glutamine (158–1761 mg L−1), glutamate (807.6–2159.7 mg L−1), ammonium (62.8–117.8 mg L−1), and potassium (531.6–685. 3 mg L−1) were 0.17 ± 0.13 × 106 cells/mL, 6.8 ± 7.1%, 0.24 ± 0.14 g L− 1, 3.3 ± 3.5 mg L− 1, 61 ± 52 mg L− 1, 52 ± 51 mg L− 1, 2.1 ± 1.9 mg L− 1 and 8.6 ± 8.6 mg L− 1, respectively. The errors achieved were like those for bioprocesses using a highlighted animal cell host (CHO cell) in similar ranges.
[Display omitted]
•Monitoring of Rabies virus-like particles production using offline Raman spectra.•Accuracy was alike between chemometrics models for samples with and without cells.•Nonlinear models showed a better fit than linear ones in biochemical monitoring.•The fitted models can be used in high-throughput upstream process development.•Variables absolute error was like those for other host cells covering alike ranges. |
ArticleNumber | 109013 |
Author | Lee Ho, Paulo Consoni Bernardino, Thaissa Tonso, Aldo Aragão Tejo Dias, Vinícius Mancini Astray, Renato Oliveira Guardalini, Luis Giovani da Silveira, Suellen Regina Fernández Núñez, Eutimio Gustavo Leme, Jaci Attie Calil Jorge, Soraia |
Author_xml | – sequence: 1 givenname: Luis Giovani surname: Oliveira Guardalini fullname: Oliveira Guardalini, Luis Giovani organization: Laboratório de Biotecnologia Viral, Instituto Butantan, Av Vital Brasil 1500, São Paulo CEP 05503-900, SP, Brazil – sequence: 2 givenname: Vinícius surname: Aragão Tejo Dias fullname: Aragão Tejo Dias, Vinícius organization: Laboratório de Engenharia de Bioprocessos. Escola de Artes, Ciências e Humanidades (EACH), Universidade de São Paulo, Rua Arlindo Béttio, 1000, São Paulo CEP 03828-000, SP, Brazil – sequence: 3 givenname: Jaci surname: Leme fullname: Leme, Jaci organization: Laboratório de Biotecnologia Viral, Instituto Butantan, Av Vital Brasil 1500, São Paulo CEP 05503-900, SP, Brazil – sequence: 4 givenname: Thaissa surname: Consoni Bernardino fullname: Consoni Bernardino, Thaissa organization: Laboratório de Biotecnologia Viral, Instituto Butantan, Av Vital Brasil 1500, São Paulo CEP 05503-900, SP, Brazil – sequence: 5 givenname: Renato surname: Mancini Astray fullname: Mancini Astray, Renato organization: Laboratório Multipropósito, Instituto Butantan, Av. Vital Brasil 1500, São Paulo CEP 05503-900, SP, Brazil – sequence: 6 givenname: Suellen Regina surname: da Silveira fullname: da Silveira, Suellen Regina organization: Pensalab, Rua Minerva, 129 Perdizes, São Paulo CEP 05007-030, SP, Brazil – sequence: 7 givenname: Paulo surname: Lee Ho fullname: Lee Ho, Paulo organization: Centro Bioindustrial, Instituto Butantan, Av. Vital Brasil 1500, São Paulo CEP 05503-900, SP, Brazil – sequence: 8 givenname: Aldo orcidid: 0000-0001-8498-7348 surname: Tonso fullname: Tonso, Aldo organization: Laboratório de Células Animais, Departamento de Engenharia Química, Escola Politécnica, Universidade de São Paulo, Av. Prof. Luciano Gualberto, Travessa do Politécnico, 380, São Paulo CEP 05508-010, SP, Brazil – sequence: 9 givenname: Soraia surname: Attie Calil Jorge fullname: Attie Calil Jorge, Soraia organization: Laboratório de Biotecnologia Viral, Instituto Butantan, Av Vital Brasil 1500, São Paulo CEP 05503-900, SP, Brazil – sequence: 10 givenname: Eutimio Gustavo surname: Fernández Núñez fullname: Fernández Núñez, Eutimio Gustavo email: egfnunez@usp.br organization: Laboratório de Engenharia de Bioprocessos. Escola de Artes, Ciências e Humanidades (EACH), Universidade de São Paulo, Rua Arlindo Béttio, 1000, São Paulo CEP 03828-000, SP, Brazil |
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Keywords | Bioprocess Monitoring Principal Component Regression Support Vector Machine Artificial Neural Network Partial Least Squares Virus-Like Particles |
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