Making better use of Spectral and Chromatographic Data in product and process development
Working with spectral and chromatographic data often results in noisy data. The good news is, there is a simple and efficient way to extract the signals from the noise, enabling a deeper and better understanding of processes and products.
If you are working on product or process development problems, you might need to utilise spectral and chromatographic data to understand and confirm that the process is performing optimally and generates the desired product. Frequently, we may summarise these data by using estimates based on peak height, position, and shape. But using these summaries inevitably causes us to lose some information about our process. This can reduce the insights we gain from our data analysis process and can negatively impact the quality of our decisions.
Spectral and related data are inherently noisy. This webinar will present a simple and efficient way of extracting the signals from the noise for a deeper, better understanding of your processes and products. If you are a scientist or engineer collecting spectral, chromatographic, or similar data and want to gain deeper insight into the trends and outcomes, this webinar is for you.
Presenter: Florian Vogt (Systems Engineer JMP, JMP Statistical Discovery)
Before joining SAS Institute, Florian worked as a process engineer in research and development at Südzucker AG, where he was involved in process optimization, product development and process evaluation. Florian Vogt holds a Master of Science in Biotechnology.
Presenter: Emmanuel Romeu (Systems Engineer JMP, JMP Statistical Discovery)
Emmanuel is a pre-sales consultant for the chemical industry. Prior to joining JMP, Emmanuel worked for 25 years at Beckman-Coulter > Diagnostic where he successively worked in industrialization, R&D in medical diagnostics and finally as a statistician. He graduated in biological engineering from the University of Clermont-Ferrand and in statistics from the University of Strasbourg.
