1H NMR-based machine learning methods for rapid authentication and composition profiling of crude palm oil

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.