Calibration

by

GCC 2024 – Machine Learning for Spectroscopy Calibration

GCC 2024Venue:
October 15-16, 2024
Moody Gardens Convention Center, Galveston, Texas 77554

Presented by: Brian Rohrback – Infometrix, Inc.

Abstract Number: 119

Abstract: Artificial intelligence and machine learning are inevitable results of the work driven by the consumer side of our economy. The question is not whether it will impact refining and chemical plant operation, but how soon and how long it will take for the benefits to outstrip the costs.  The goal is to provide practical guidance for making progress in this complicated set of fields. Machine Learning is critical to interpreting output from any type of spectrometer and improves the flow of information providing a significant leg up for process understanding. The key is to fully automate spectroscopic calibration. Gulf Coast Conference 2024

by

IFPAC 2024 – Chemometrics in the Cloud

IFPAC 2024Venue: March 3-6, 2024 Bethesda North Marriott Hotel and Conference Center Presented by: Brian Rohrback, Ph.D., MBA, President, Infometrix, Inc. Abstract: In the spirit of automation, there are cloud-based tools from both the chemometrics and the general statistics realms that can be applied to simplify the work involved in optimizing a calibration. Robust statistical techniques require some set-up of parameters, but once established for an application, they are often usable in every other instance of that application. The result is a one-pass automated means of selecting optimal samples for a calibration problem and, in turn, simplifies and automates the assignment of model rank. In the end case, this means that a spectrometer can essentially become an appliance; take it out of the box, plug it in, and enjoy. The capability exists to have a spectrometer self-tune and adapt to a specific application, then keep the spectrometer in appropriate calibration completely through closed-loop control. Automation of best practices needs to include how to match laboratory reference data to spectral data, an unbiased approach to selecting validation samples, an optimal mechanism for model construction, establishing standards for quality reports, tracking model performance over time, handling process or ingredient transitions, and much more. Register at www.IFPACglobal.org/attendee-registration.
by

IFPAC Conference 2024

IFPAC Conference 2024

IFPAC 2024

March 3-6, Washington, D.C.

The IFPAC conference is one of the premier settings to exchange ideas on the future of manufacturing and the quality control that is needed across all industries. Much of the focus is on the pharmaceutical and biotech industries, but there are discussions that span all manufacturing industries from consumer products to oil and chemicals.

CHEMOMETRICS & ADVANCED SEPARATIONS TRACK

Chemometrics – COPA (Chemometrics for Online Process Analysis)
Chairs: Brian Rohrback, Infometrix, Bo Gong, Dow, Christian Airiau, Sanofi and Neal Gallagher, Eigenvector

The IFPAC Chemometrics session is focusing on the organization of analytical libraries (primarily optical spectroscopy) and the efficient use and integration of chemometric principles in support of an industry initiative by US Pharmacopeia to establish guidelines and standards for calibration. Participation by industry leaders, instrument company scientists, and chemometrics experts is on the schedule.

Join Brian Rohrback in March 2024 for this important event.

by

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.