Calibration

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IFPAC 2023 – Speed Dating Chemometrics and Machine Learning

IFPAC 2023 ConferenceVenue: June 4, 2023, 8:30am – 12:00pm Bethesda North Marriott Hotel and Conference Center Presented by: Brian Rohrback, Ph.D., MBA, President, Infometrix, Inc. Barry M. Wise, Ph.D., President, Eigenvector Research, Inc.     Course Description: There is a lot of confusion on what constitutes best practices in the application of multivariate statistics to laboratory, process, and field analytics. The terminology in use does not always clarify and most of the time a technique touted in the literature is not compared to any other technology that could be applied to the same problem. Tools from chemometrics and machine learning categories benefit from some user experience and this course is aimed at refreshing the basics of the field of multivariate analysis and data visualization, supplying applications that tie to routine product quality maintenance, and focusing in on the most common use of the algorithms – those employed in instrument calibration. An introduction to data visualization and exploratory data analysis techniques such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) will be covered along with the practical basis for their use. A wide variety of examples will be shown ranging from laboratory analysis, in-line and on-line process monitoring, and field applications. Infometrix President Brian Rohrback will cover the thought process that helps organize and complete the implementation of a bespoke evaluation system. Calibration models are a critical part of spectroscopic and other methods in Process Analytical Technology (PAT) and in the laboratory. But there’s more to getting a good calibration model than simply measuring a few samples and doing a Partial Least Squares (PLS) regression. The process starts with planning for calibration samples and ends with deployment and maintenance considerations. Eigenvector Research President Barry M. Wise covers the steps required to produce a quality calibration model, including data screening, visualization, and model creation. Also covered will be common mistakes and how not to make them. These topics are designed to cover the best practices of chemometrics technology and will prepare participants for tackling a vast array of problems. This course is useful for any scientist concerned with optimizing their analytical methods to get the most out of either laboratory or process operations. To Register: This course will be presented prior to the IFPAC Annual Meeting. Register at www.IFPACglobal.org/attendee-registration.
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Upcoming Events 2022

We miss in person relationships and live experiences that are not available with virtual and hybrid meetings. Virtual platforms have their issues which makes us desire more for in person gatherings. We’re still not back to normal but we are easing our way back. Hope you feel the same and will join us at these upcoming events to share and engage in a valuable discussion. Upcoming Events in 2022 CPAC, Seattle, WA, May 2-3

Routine Quality Assessment – Similarities and uniqueness of machine learning and chemometrics and how they combine to form robust solutions.

PEFTEC, Rotterdam, June 8-9 Streamlining the Use of Chemometrics – Faster response, improved flow of information and a significant process understanding nearly cost-free. IFPAC, Bethesda, MD, June 12-15

Agile Process Analytics – Combining technical tools to augment or replace tasks that consume brainpower for timely response and greater profits with future goals of optimization with automated spectroscopy calibration.

SciX, Kentucky, Oct 2-7

Optimizing Spectroscopy Performance – Lessening the workload with automation of models and maintaining them quickly and easily for robust, reliable, and timely calibrations.

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IFPAC 2022 – Agile Process Analytics

Join Brian Rohrback of Infometrix, Inc. for his talk on Agile Process Analytics, June 14, 1:05pm EST. Agile Process Analytics Application knowledge and chemometrics play a vital role in the processing of all types of multivariate data into application-specific information and has been doing so for at least 50 years.  There has been a not-so-subtle shift in thinking as we integrate basic concepts and the occasional hallucination in the data mining, artificial intelligence, machine learning worlds.  The target is to identify combinations of our technical tools to augment or replace tasks that consume brainpower where timely response is valued, and profits are at risk.  The biggest focus of chemometrics has been in the calibration of optical spectrometers.  It is worth considering the subtasks:
  1. Optimizing the instrument settings for a given application;
  2. Optimizing the method parameters – preprocessing, transformations, wavelength ranges;
  3. Handling of calibration transfer; and
  4. Optimizing models for inliers and rank in pursuit of routine processing and adjusting to changes in ingredients and unit operation.
The first two tasks are a set-once method development and the third may be generic across all applications. This paper tackles subtask 4 with a project that combined traditional approaches in statistics, database organization, pattern recognition, and chemometrics with some newer concepts tied to better understanding of data mining, neurocomputing, and machine learning.  The future goal is to automate spectroscopy calibrations such that it is possible to have instrument systems tune themselves.
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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.