Novel Analytical Workflow for Tracing Synthetic Caffeine Adulteration in Amazonian Guaraná ( Paullinia cupana ) Using Low-Field 1H NMR Fingerprinting and Chemometrics

Abstract

Maués guaraná (Paullinia cupana), a Brazilian product with Geographical Indication (GI), is highly susceptible to economically motivated fraud, particularly through the undeclared addition of synthetic caffeine. Conventional chromatographic methods for authenticity control are time-consuming, costly, and rely on hazardous chemicals. This study presents a comparatively streamlined, solvent-efficient, and environmentally friendly approach based on low-field 1H NMR spectroscopy coupled with chemometrics, eliminating the need for deuterated solvents. Using authentic (n = 40), commercial (n = 18), and adulterated samples (1–10% synthetic caffeine), the data-driven soft independent modeling of the class analogy (DD-SIMCA) model achieved 100% sensitivity and over 99% accuracy for authentication. Simultaneously, a PLS regression model accurately quantified adulteration levels, showing high predictive performance (Rpred2 = 0.9891; RMSEP = 0.36%) and low detection limits. This orthogonal workflow successfully distinguished natural agro-industrial variability from intentional adulteration, demonstrating the potential of a practical, affordable, and sustainable first-line screening approach for future regulatory application.