Biomechanism and Bioenergy Research

Biomechanism and Bioenergy Research

Prediction of Pomegranate Seed Oil Oxidation Kinetics Under Sunlight Exposure Using Chemometric Modeling

Document Type : Original Research

Authors
1 Biomedical Engineering Group, Department of Electrical and Information Technology, Iranian Research Organization for Science and Technology (IROST), 33535111, Tehran, Iran
2 Department of Chemical Technologies, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran
10.22103/bbr.2026.27387.1156
Abstract
Pomegranate seed oil (PSO) is highly valued for its punicic acid content but is extremely susceptible to oxidation, while traditional quality assessment methods are time-consuming and require hazardous chemicals. This study aimed to develop a rapid, non-destructive method for predicting PSO quality using ¹H NMR spectroscopy combined with chemometric modeling. Spectra were preprocessed (alignment, normalization, binning at 0.2 ppm into 38 bins). Partial Least Squares (PLS) regression predicted peroxide value (PV), p-anisidine value (p-AV), acid value (AV), and totox value from NMR spectra. The PLS model with one latent variable predicted totox with high accuracy (R² = 0.892). Prediction accuracy was also good for p-AV (R² = 0.880), AV (R² = 0.967), and PV (R² = 0.886). Degradation rates showed PV increased rapidly during the first two days (3.73 units/day) then slowed (1.21–1.43 units/day), while p-AV increased at a constant rate (4.84–4.97 units/day). Principal Component Analysis revealed that one component explained 99.6% of total variance, confirming all indices reflect the same oxidative process. The optimal weighting factor for combining PV and p-AV was exactly 2:1, validating the traditional totox formula. This proof-of-concept study demonstrates that ¹H NMR spectroscopy combined with PLS regression shows promise as a rapid and non-destructive method for monitoring pomegranate seed oil quality during photo-oxidation. However, further validation with larger, independent datasets is required before routine application in quality control settings.
Keywords

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Articles in Press, Accepted Manuscript
Available Online from 30 September 2026

  • Receive Date 07 June 2026
  • Revise Date 11 July 2026
  • Accept Date 15 August 2026