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1H NMR-based machine learning methods for rapid authentication and composition profiling of crude palm oil
Author(s)
Nggofur, Abdul
Sueviriyapan, Natthapong
Nuntawong, Noppadon
Sutthiumporn, Ketsada
Chirachanchai, Suwabun
Jongpatiwut, Siriporn
Date Issued
September 1, 2026
Type
Article
Abstract
A rapid analytical workflow for determining geographical origin and predicting fatty acid composition of crude palm oil (CPO) was developed using 1H NMR, GC-FID, and machine learning. Analyzing CPO samples from Indonesia, Malaysia, the Philippines, and Thailand using unsupervised fingerprinting with principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) revealed partial origin-based grouping. Supervised classification, validated via leave-one-out cross-validation (LOOCV) and uncertainty quantification (UQ), reliably discriminated the origins above random chance. Additionally, partial least squares regression (PLSR) accurately predicted oleic, linoleic and myristic acid levels measured by GC-FID, whereas the accuracy decreased for lauric, stearic and palmitic acids. PLSR reliability was rigorously validated using latent variable selection and permutation testing to rule out random correlations. Overall, this integrated 1H NMR and machine learning approach offers a rapid tool for CPO geographical traceability and compositional evaluation, demonstrating its potential for industrial quality control.
Citation
Food Chemistry, 522, 2026