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