Beyond Chemical Fingerprinting: Raman Photonics, Molecular Spectroscopy and Explainable Artificial Intelligence for Palm Oil Authentication, Quality Assessment and Transferable Optical Sensing

Abstract

Authenticity verification and quality control in palm oil increasingly require rapid, defensible measurements across heterogeneous supply chains. This critical qualitative literature review examines the molecular physics, analytical performance, and interpretability challenges associated with Raman photonics and complementary optical spectroscopies. Drawing on journal evidence published between 2020 and 2026, it distinguishes chemically grounded measurement from statistical identification that may depend on correlated pigments, processing conditions, or instrument characteristics. Recent handheld Raman studies demonstrate geographical discrimination of West African palm oils, while machine learning-assisted Raman analysis has reported promising prediction of peroxide, iodine, and acid values. However, high within-study accuracy does not establish causal provenance markers, robustness across refining states, or transferability to new instruments and regions. The review compares Raman, Fourier transform infrared, near-infrared, fluorescence, and multimodal approaches; critically evaluates spectral preprocessing, variable selection, explainable AI, calibration transfer, and uncertainty management; and proposes an evidence-gated framework for deployment. Key recommendations include chemically verified band assignments, hierarchical out-of-domain validation, metrological controls, prediction intervals, standardized acquisition metadata, and reference-laboratory confirmation of consequential decisions. The resulting framework positions explainable spectroscopy as a powerful screening and process-monitoring capability while maintaining clear boundaries between compositional inference, product authentication, geographic attribution, and certified sustainability traceability.