Spectroscopy

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Pittcon Conference + Exposition 2026

Pittcon 2026Speed Dating Chemometrics and Machine Learning

Tuesday, March 10, 2026 1:30 – 3:00 PM · (America/Chicago), Room 006B

San Antonio, TX | Henry B. González Convention Ctr

Details and registration for the session on Speed Dating Chemometrics and Machine Learning, presented by Brian Rohrback, can be found here or you can contact info@infometrix.com for more information.

Abstract
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. The focus here is to refresh the basics of the field of multivariate analysis and data visualization plus how the history of the field now ties to machine learning. The course illustrates the principles of chemometrics as they apply to routine product quality maintenance, primarily on the most common use of the algorithms in organizing the information flow from sensors, spectrometers, and chromatographs.
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. The course covers the thought process that helps organize and complete the implementation of an application-specific evaluation system.
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.
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Analyzer Technology Conference, Booth #511

Join Infometrix at ATC 2026 Conference in booth #511 for presentation on Ai-Metrix and the automation of chemometric calibrations.

April 13-17, 2026

Galveston Island Convention Center

Meet with industry leaders for discussion on new and innovative analyzer techniques, developments, and applications for process and laboratory measurements as well as the fundamentals of quality control employing optical spectroscopy.

For additional information on the Analyzer Technology Conference, brochure and event overview in pdf are available for viewing with links below. You can also reach out to info@infometrix.com as well for any questions. We look forward to seeing you.

ATC Brochure

ATC Event Overview

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CPACT Webinar on The Intersection of Machine Learning, Chemometrics, and Spectroscopy

CPACT Webinar on The Intersection of Machine Learning, Chemometrics, and Spectroscopy

Presented by Brian Rohrback of Infometrix, Inc.

April 23, 2026 (7:00PM UK Time).

See abstract below. Visit CPACT Webinars or contact info@infometrix.com for details

AI and machine learning have stormed into our scientific and marketing lexicons.  As we discuss the integration into analytical chemistry applications, we face the invariable need to merge with the field of chemometrics.  We know chemometrics as an area of study that has generated a set of tools for practitioners to use in extracting the information content from sets of analytical data.  Machine learning is the extension of this idea, just without human intervention.  As we employ the tools provided by chemometrics to autonomously automate a process, where the computer is making decisions based on the input data, the chemometrics becomes a cog in the machine learning world.  One area ripe for this combination is optical spectroscopy, particularly IR, NIR, and Raman.

Let’s do a thought experiment.  What if we decided we wanted to fully automate the use of optical spectroscopy for a quality control application?  What would be required to take any spectroscopy instrument, put it into a lab or process stream, have it learn the application, build an optimized model, deploy the model for QC, and maintain the calibration for the life of the instrument. Can this be done without human interaction?

To standardize the control of spectroscopy assessments, there are four primary software-related areas to tackle, two of which the user may only need to do once.

  1. At the start, a method needs to be set that optimizes how future spectra will be manipulated and involves algorithm selection, choice of preprocessing, and potentially trimming the wavelength range.
  2. The other early process is to understand the precision of the laboratory methods, how they impact calibration models, and how this information needs to be factored into understanding system performance.
  3. On a continuous basis, the process chemistry can change dictating a maintenance effort to determine the optimum number of factors and identify outliers that negatively impact model performance.
  4. A system has been outlined by ASTM to automatically flag when the model performance has degraded.

Infometrix has spent the last decade and a half commercializing a system designed to fully automate and efficiently optimize all aspects of the above calibration. Components of the thought experiment are in place and a discussion of the approach (plus solutions to encounters with implementation quicksand) shows how to blend chemometrics into machine learning for the benefit of industry.

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IFPAC 2026 Presentations

IFPAC 2026SAVE THE DATE FOR IFPAC-2026! Network and share your knowledge on advancements in manufacturing science. Join Brian Rohrback for the following presentations.

Chemometrics in Chromato-Context (ID# 83)

Chromatography is one of the most useful technologies to employ for routine chemical assessment in industry. In many cases, it is the cheapest and most adaptable technology available to fully document the composition of our samples. Chemometrics has been used to interpret chromatographic traces, although the implementation has been far less than seen in spectroscopic applications.  It gives us the chance to review where chemometrics has been utilized in the chromatographic sciences and where the advantages lie.   Starting with the chromatography basics, this presentation builds up the world of chemometrics step-by-step to show where the technology has been used and can contribute in the form of driving much more reliable results from the data we collect.

Fully Integrated Data Analysis (ID# 84)

We employ many sources of analytical information to perform quality control on the processes we manage.  In many cases, we are not utilizing the information content from the data we currently collect.  In most quality control situations, results from different sources will need to be merged into a single release metric. This can be done hierarchically, where information will need to be factored in order of priority or response time.  Another option is to process simultaneous data in a blended, data fusion model.    Care must be taken to ensure that the complexity of fusing several sources of data does not involve so much complexity that the system is unwieldy or simply cannot be used. Here we will discuss current techniques and show how the value of the information stream can be improved by more timely integrated data analysis.  An example from the pharmaceutical classification of botanicals shows the power of this approach.

Chemometrics – COPA (Chemometrics for Online Process Analysis)

Chairs: Brian Rohrback, Infometrix, Antonio Benedetti, Polymodelshub, and Hossein Hamedi, Arrantabio

Chemometrics is central to all calibration work in spectroscopy and has influence in most of the instrumentation tied to product quality control. We are investigating the challenges and the successes tied to the implementation of chemometric technology as it relates to the process industry, whether for pharmaceuticals, for consumer products, for food, or for chemicals. We seek to optimize quality control.

Globalized Spectroscopy (ID#261)

Implementing spectroscopy applications is often a complex management process, even if the deployment is restricted to a single spectrometer.  When a company wants to roll out spectroscopy in multiple locations additional potential problems arise however, managed properly, the benefits are significant.  IR, NIR, and Raman are the most common optical systems employed to measure chemistry in a quality control application, but they require a calibration to convert spectral signatures to the properties of interest.  Unless an objective mechanism for performing calibration is available across the sites, product quality results will vary.  It is possible to package “best practices” into a system that forces consistency and optimal outcome.  By removing the subjective nature of manual calibrations, the quality of the quality control can be assessed and maintained at a high level.

View the program preview here.

Contact info@infometrix.com for questions or for more information on presentations and event details.

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SciX 2025 Conference – On Machine Learning, PLS, and Local Weighting

Venue: Northern Kentucky Convention Center, Covington, KY

Date: Oct 5 – 10, 2025

Brian Rohrback will be presenting in the session on Artificial Intelligence and Machine Learning in Process Analytical Technology (PAT). See abstract below.

On Machine Learning, PLS, and Local Weighting

AUTHOR: Brian Rohrback
ABSTRACT:
A dozen years ago, Infometrix embraced the target of completely automating the installation and maintenance of any optical spectrometer and for any application. Ultimately, the goal is to identify a generic machine learning approach that can be taken to mimic the results that an experienced chemometrician would achieve if charged with producing an optimized model.  The idea has been presented in numerous publications and this specific work was triggered initially by Workman et al. (1995). Clearly, the chemometrics tasks can be broken down, assigning best practices procedures for each. One part of this process is choosing the algorithmic approach. Partial Least Squares (PLS) is the workhorse and is incorporated into nearly every spectroscopy system.   In cases of non-linearity, a locally weighted application of PLS will avoid the failures of non-linear methods.  Local models can also simplify model maintenance as conditions (spectrometer, ingredients, unit operations) change.

Automation of Local Regression Model Building for Spectroscopic Data – Journal of Chemometrics