Webinar on
The
Intersection of Machine Learning, Chemometrics, and Spectroscopy
Brian
Rohrback, Informetrix
23rd
April 2026 at 2pm EDT, 7pm UK time
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.
- 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.
- 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.
- 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.
- 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.
This webinar
will take no longer than one hour.
The webinar is
for CPACT members only.
Please register directly at https://universityofstrathclyde.webex.com/weblink/register/r9ad5d012654525ca76b739e863a34d27
