{"title":"激光波长和样品调节对拉曼光谱法监测 SARS-CoV-2 VLP 生产上游阶段生化过程的影响","authors":"","doi":"10.1016/j.bej.2024.109441","DOIUrl":null,"url":null,"abstract":"<div><p>This work assessed the impact of laser wavelength and sample conditioning on offline monitoring (viable cell density, cell viability, virus titer, glucose, lactate, glutamine, glutamate, and ammonium) of SARS-CoV-2 virus-like particles production upstream stage by Raman spectroscopy. The evaluated chemometrics techniques were Partial Least Squares (PLS) and Artificial Neural Networks (ANN), and different spectral filtering approaches were also considered. ANN showed better prediction capacity for most of the parameters, but ammonium and lactate, better predicted by PLS, and glutamine, no difference between modeling techniques was detected. For cell growth parameters and virus titer, samples without cells measured at 785 nm Raman laser wavelength originated better-adjusted models. This laser wavelength was also more appropriate for the remaining monitored experimental parameters except for glucose, in which the best model came from the spectral database acquired at 1064 nm wavelength. Cell remotion significantly increased the accuracy of viable cell density, cell viability, glutamate, and virus titer models. However, glucose, lactate, and ammonium models showed better prediction capacity for samples containing cells. Thus, it was demonstrated that laser wavelength, sample conditioning, spectral preprocessing, and chemometric modeling techniques need to be tailored for each experimental parameter to improve accuracy.</p></div>","PeriodicalId":8766,"journal":{"name":"Biochemical Engineering Journal","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Laser wavelength and sample conditioning effects on biochemical monitoring of SARS-CoV-2 VLP production upstream stage by Raman spectroscopy\",\"authors\":\"\",\"doi\":\"10.1016/j.bej.2024.109441\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This work assessed the impact of laser wavelength and sample conditioning on offline monitoring (viable cell density, cell viability, virus titer, glucose, lactate, glutamine, glutamate, and ammonium) of SARS-CoV-2 virus-like particles production upstream stage by Raman spectroscopy. The evaluated chemometrics techniques were Partial Least Squares (PLS) and Artificial Neural Networks (ANN), and different spectral filtering approaches were also considered. ANN showed better prediction capacity for most of the parameters, but ammonium and lactate, better predicted by PLS, and glutamine, no difference between modeling techniques was detected. For cell growth parameters and virus titer, samples without cells measured at 785 nm Raman laser wavelength originated better-adjusted models. This laser wavelength was also more appropriate for the remaining monitored experimental parameters except for glucose, in which the best model came from the spectral database acquired at 1064 nm wavelength. Cell remotion significantly increased the accuracy of viable cell density, cell viability, glutamate, and virus titer models. However, glucose, lactate, and ammonium models showed better prediction capacity for samples containing cells. Thus, it was demonstrated that laser wavelength, sample conditioning, spectral preprocessing, and chemometric modeling techniques need to be tailored for each experimental parameter to improve accuracy.</p></div>\",\"PeriodicalId\":8766,\"journal\":{\"name\":\"Biochemical Engineering Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biochemical Engineering Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1369703X24002286\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biochemical Engineering Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1369703X24002286","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
Laser wavelength and sample conditioning effects on biochemical monitoring of SARS-CoV-2 VLP production upstream stage by Raman spectroscopy
This work assessed the impact of laser wavelength and sample conditioning on offline monitoring (viable cell density, cell viability, virus titer, glucose, lactate, glutamine, glutamate, and ammonium) of SARS-CoV-2 virus-like particles production upstream stage by Raman spectroscopy. The evaluated chemometrics techniques were Partial Least Squares (PLS) and Artificial Neural Networks (ANN), and different spectral filtering approaches were also considered. ANN showed better prediction capacity for most of the parameters, but ammonium and lactate, better predicted by PLS, and glutamine, no difference between modeling techniques was detected. For cell growth parameters and virus titer, samples without cells measured at 785 nm Raman laser wavelength originated better-adjusted models. This laser wavelength was also more appropriate for the remaining monitored experimental parameters except for glucose, in which the best model came from the spectral database acquired at 1064 nm wavelength. Cell remotion significantly increased the accuracy of viable cell density, cell viability, glutamate, and virus titer models. However, glucose, lactate, and ammonium models showed better prediction capacity for samples containing cells. Thus, it was demonstrated that laser wavelength, sample conditioning, spectral preprocessing, and chemometric modeling techniques need to be tailored for each experimental parameter to improve accuracy.
期刊介绍:
The Biochemical Engineering Journal aims to promote progress in the crucial chemical engineering aspects of the development of biological processes associated with everything from raw materials preparation to product recovery relevant to industries as diverse as medical/healthcare, industrial biotechnology, and environmental biotechnology.
The Journal welcomes full length original research papers, short communications, and review papers* in the following research fields:
Biocatalysis (enzyme or microbial) and biotransformations, including immobilized biocatalyst preparation and kinetics
Biosensors and Biodevices including biofabrication and novel fuel cell development
Bioseparations including scale-up and protein refolding/renaturation
Environmental Bioengineering including bioconversion, bioremediation, and microbial fuel cells
Bioreactor Systems including characterization, optimization and scale-up
Bioresources and Biorefinery Engineering including biomass conversion, biofuels, bioenergy, and optimization
Industrial Biotechnology including specialty chemicals, platform chemicals and neutraceuticals
Biomaterials and Tissue Engineering including bioartificial organs, cell encapsulation, and controlled release
Cell Culture Engineering (plant, animal or insect cells) including viral vectors, monoclonal antibodies, recombinant proteins, vaccines, and secondary metabolites
Cell Therapies and Stem Cells including pluripotent, mesenchymal and hematopoietic stem cells; immunotherapies; tissue-specific differentiation; and cryopreservation
Metabolic Engineering, Systems and Synthetic Biology including OMICS, bioinformatics, in silico biology, and metabolic flux analysis
Protein Engineering including enzyme engineering and directed evolution.