EAS 2021 – Multivariate Lessons for the Analytical Chemist, Scott Ramos

EAS 2021 Multivariate Lessons for the Analytical ChemistScott Ramos, recipient of the 2021 EAS Award for Outstanding Achievements in Chemometrics, will give his talk on “Multivariate Lessons for the Analytical Chemist”. Join us November 16th at 11am.

Multivariate Lessons for the Analytical Chemist

The field of chemometrics is a relatively new discipline, with its genesis about 40 years ago. Despite this relatively short history, it is now thriving as a vibrant area of research and practice, finding place in a wide variety of chemical applications. Some of the tools of the trade have found acceptance well outside of chemistry. For example, PLS (Partial Least Squares Regression) has become popular in statistics which was lacking methods for data with highly correlated variables.

Two main thrusts have propelled the evolution of chemometrics: innovation and evangelism. The toolkit available to practitioners is continually being augmented by improvements to current methods as well as creation of new algorithms. But, for the field to succeed, it must reach the hands of those who can benefit: the many analysts who utilize these tools in their daily work.

To meet the needs of already-overworked analysts, working solo or in teams, we can identify at least 3 ways in which multivariate tools can become part of the company work practice: hire a dedicated chemometrician, train one or more of staff members in the use of chemometrics, or contract an outside entity to provide chemometrics services.

Whichever the pathway a company chooses, understanding how best to optimize chemometrics models is paramount to success. This talk will review some of these concepts, covering issues that impact model quality, and will reflect on the viability of automating these decisions.


Free Webinar: Development of Automated Chemometric Platform for Accelerated Raman-based Model Optimization in Biologics

BioPharma-Asia webinar registrationBrian Rohrback, president of Infometrix, will join Oliver Steinhof, PAT Scientist at Biogen and Nicolas Langenegger, Senior Associate Scientist at Biogen for this free webinar on September 20, 2021 at 10:00am EST.

Register at: biopharma-asia.com

The increasing use of multivariate models both as part of the control strategy in commercial (bio)pharmaceutical production as well as for process monitoring calls for an efficient strategy for model development and model life cycle management. The traditional approach to develop multivariate models based on spectroscopy involves manual data management such as selection and transfer of spectroscopic data, import into modeling software and selection/exclusion of data. That is followed by addition of reference data, alignment of time stamps and import into the modeling software. 90% of the time required to construct a multivariate model is spent on data preparation. It was decided to develop a solution to automate these steps to prepare (stage) the data required for model development, reducing the time required to prepare a typical set of batch data to about five minutes. A second tool was developed to automatically optimize data pretreatment parameters and spectral range for PLS models. Both tools allow our scientists to invest their time into more value-added activities.