Calibration

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Automating the Optimization of Locally Weighted Models is a Solution

Automation of Local Regression Model Building for Spectroscopic Data, JChem2024 Pell et alA calibration model tends to improve as additional calibration samples are added to the library. If the samples reflect variation in the chemical composition, the model then expands its zone of relevance. Ultimately, as these new zones expand calibration scope, the model can degrade in performance due to nonlinearities and may require adding to the model rank, which can make the model fragile. Building local models is one answer to gain the best of both worlds, but optimizing a locally weighted model is tricky and time consuming. Automating the optimization of locally weighted models is a solution.

http://doi.org/10.1002/cem.3637

For additional information or questions, contact info@infometrix.com.

#chemometrics #LWR #spectroscopy #regression

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LARTC 2024 – Find us at booth 52

Venue:
September 3-5, 2024
Cartagena, Colombia

Booth: 52

Latin American Refining Technology Conference (LARTC) is the number one downstream event in Latin America where major refiners will gather to network and share the latest in technology and collaboration opportunities. Infometrix will be at booth #52. Come visit us as we share solutions to save time, resources, and to reduce manual workload. Eliminating retention time shifts in chromatography and automating calibration for optical spectrometers to enable better models and optimize use of personnel will be the focus. We look forward to seeing you. LARTC 2024

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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

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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.