Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models

Gilbraith, William E.; Carter, J. Chance; Adams, Kristl L.; Booksh, Karl S.; Ottaway, Joshua M.

doi:10.3390/molecules26237281

Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models

Journal Article · Mon Nov 29 23:00:00 EST 2021 · Molecules

DOI:https://doi.org/10.3390/molecules26237281· OSTI ID:1868159

Gilbraith, William E. ^[1]; Carter, J. Chance ^[2]; Adams, Kristl L. ^[2]; Booksh, Karl S. ^[1]; Ottaway, Joshua M. ^[2]

Univ. of Delaware, Newark, DE (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1868159

Report Number(s):: LLNL-JRNL-835333; 1045421

Journal Information:: Molecules, Journal Name: Molecules Journal Issue: 23 Vol. 26; ISSN 1420-3049

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models

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