Regression

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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

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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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Analysis of Biodiesel-Diesel Blended Fuels Using Ultrafast Gas Chromatography (UFGC) and Chemometric Methods

CSC 2018 – 101st Canadian Chemistry Conference and Exhibition, Edmonton, AB, Canada, May 27-31, 2018

Title:
Analysis of Biodiesel-Diesel Blended Fuels Using Ultrafast Gas Chromatography (UFGC) and Chemometric Methods

Authors:
Amber M Hupp, College of the Holy Cross (Primary Presenter)
Joseph Perron, Falcon Analytical
Ned Roques, Falcon Analytical
John Crandall, Falcon Analytical
Scott Ramos, Infometrix
Brian Rohrback, Infometrix

Division:
Analytical Chemistry (AN)

Symposium:
Analytical Separations: Theory, Applications, Instrumentation

Abstract:
Biodiesel is added to diesel fuel in concentrations ranging from 2 to 35%, mostly for the purposes of reducing greenhouse gases. The resulting binary blended fuels (labeled B2-B35) have lowered hydrocarbon and carbon monoxide emissions compared to petrodiesel. Traditional analysis methods utilize gas chromatography (GC) with a long, polar column leading to separation times of thirty to sixty minutes.  As a faster alternative, we propose using ultrafast GC (UFGC) to evaluate binary fuels with concentrations ranging from 1-20% with various biodiesel feedstocks (soybean, tallow, safflower, sunflower, camelina, palm, etc). A short mid-polarity column (MXT-50, 4m x 180 mm x 0.2 mm, Restek) was used to provide optimal separation of the saturated fatty acid methyl esters (FAMEs) in the biodiesel while still allowing for retention of the variety of components in the diesel. Several chemometric tools, including Principal Component Analysis (PCA) and Partial Least Squares (PLS), were used to provide group analysis of feedstock type and concentration.