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Use process monitor5/17/2023 ![]() One application where modeling can provide a useful solution is glucose monitoring. Image Credit: Thermo Fisher Scientific-Handheld Elemental & Radiation DetectionĬompanies can take concrete actions to improve the management of their processes, thanks to the solution offered by the combination of the Ramina Process Analyzer hardware and data science tools. As the glucose levels decline over time, it is necessary to feed the bioprocess to keep it in check once a minimum value is reached. The graph below shows users where the bioprocess has been fed more glucose, as indicated by the spikes. The glucose model was applied to data outputs from a fourth Ramina system, enabling real-time monitoring of glucose concentration. The predictive model can be used with any Ramina Process Analyzer after it has been created. Knowing when to feed bioprocess with data science ![]() As a result, a predictive model was created that offers improved precision and accuracy when used with various Ramina Process Analyzer instruments. No matter where the fleet is located, these characteristics guarantee that every Raman system is incredibly accurate, stable, and consistent with other Raman systems.įrom this starting point, basic preprocessing techniques can be used that pinpoint and amplify the pertinent signals in the Raman data. Three key characteristics of the Ramina Process Analyzer-stability, reproducible sampling interface, and factory calibration-have a significant impact on how easily data can be preprocessed. Image Credit: Thermo Fisher Scientific-Handheld Elemental & Radiation Detection The power of preprocessing A “global” glucose model was created using the data after they had been cleaned (pretreated). Although the combined data at first appeared to be quite disorganized, cleaning up the data and making everything work well together was easier.ĭue to the Ramina system’s stability and the available data science tools, this task was relatively simple. The information displayed below is drawn from three data sets that were gathered from various processes using various Ramina Process Analyzers in various parts of the world.Ī robust glucose prediction model was constructed quickly. The process of gathering data is the first step in creating this model. Image Credit: Thermo Fisher Scientific-Handheld Elemental & Radiation Detection Global data for a global solution Modeling the glucose content of a bioprocess and predicting when to add more glucose to ensure that cells are produced at a precise rate are two functions of the Thermo Scientific™ Ramina™ Process Analyzer. Creating a model for glucoseĬells in the biopharmaceutical manufacturing process need a glucose-feeding cycle to support cell division. To show how simple it is to add value with an all-in-one portable and scalable Raman system, this article will break down the straightforward process of developing and implementing quantitative Raman solutions. To optimize the bioprocess, the predictive models’ deployment will provide information that can be put into practice, such as when feeds or controls should be turned on. These models can be quickly deployed to various Raman systems once they have been created and validated. The compositional information can be processed to achieve this goal using data science tools and packages that create reliable predictive models for crucial processes. ![]() The composition measurements are useful for understanding a bioprocess, but ultimately, this data should be used for optimization and control. When compared to conventional physical sampling-based measurements, the solid-state process Raman is a proven technology for gathering that data while significantly lowering maintenance and operating costs. Sponsored Content by Thermo Fisher Scientific – Handheld Elemental & Radiation Detection Reviewed by Mychealla Riceįor efficient operation, biopharmaceutical production needs a stream of compositional data that is nearly constant.
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