EAS 2021 – ChemMLometrics. Perform. Repeat. , Brian Rohrback

EAS 2021 ChemMLometrics. Perform.Repeat. Join Brian Rohrback for his talk on ChemMLometrics. Perform. Repeat., November 16th at 10:15AM.

ChemMLometrics. Perform. Repeat.

Doing a one-off research project can be very satisfying, but in industry the money is made when the results of such a project can be placed into routine quality assessment.  There is much discussion today revolving around machine learning and some of this discussion has invaded the space that has been occupied by chemometrics over the last few decades. There is a large overlap as these techniques are applied in instruments aimed at process quality control.  To a large extent, any differentiation will simply be a question of the jargon chosen. An on-line Stanford University class on machine learning covers Principal Components Analysis, Principal Components Regression, Partial Least Squares, K-Nearest Neighbor, and other mainstays of the chemometrics toolbox.  However, there are techniques that are new in the machine learning realm that can be employed for targeted tasks, improving the traditional chemometrics regime of supervised learning.  The key is to narrow the focus for each component in a system and to understand the extent to which you can control the inputs.  This talk highlights examples in optical spectroscopy calibration particularly for the identification of outliers, and in chromatography for both signal processing and the management of chromatographic libraries. The emphasis is to highlight both the similarities and the uniqueness of machine learning and chemometrics and show how they combine to form robust solutions for industry.

EAS 2021 – Automating Calibrations for Optical Spectroscopy

EAS 2021 Automating Calibrations for Optical Spectroscopy Join Brian Rohrback for his talk on Automating Calibrations for Optical Spectroscopy, November 16th at 1:30PM.

Automating Calibrations for Optical Spectroscopy

This talk represents a summary of a multi-industry consortium established eight years ago to re-evaluate how the calibration process in optical spectroscopy could be managed more efficiently.  The idea is to enable a shift from current practices to approaches that take better advantage of the computational power and some newer concepts supplied by research into machine learning algorithms.  The result is a solution that is not disruptive of any legacy instruments or software already in place and lessens the workload rather than laying on additional requirements. The approach uses all readily available components and can be assembled easily for any specified purpose.  The use of commercial components reduces the cost of deployment and assembling pieces in a plug-and-play manner minimizes the impact of any previous selections of hardware and software.

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.