Authentication of dietary supplements through Nuclear Quadrupole Resonance (NQR) spectroscopy.

Summary: As the industry grows, adulteration of many products by mislabelling, re‐branding and false advertising is becoming prevalent practice. Existing solutions for analysis often require extensive sample preparation or are limited in terms of detecting different types of integrity issues. We describe a novel authentication method based on Nuclear Quadrupole Resonance (NQR) spectroscopy which is quantitative, non‐invasive and non‐destructive. It is sensitive to small deviation in the solid‐state chemical structure of a product, which changes the NQR signal properties. These characteristics are unique for different manufacturers, resulting in manufacturer‐specific watermarks. We show that nominally identical dietary supplements from different manufacturers can be accurately classified based on features from NQR spectra. Specifically, we use a machine learning‐based classification called support vector machines (SVMs) to verify the authenticity of products under test. This approach has been evaluated on three products using semi‐custom hardware and shows promising results, with typical classification accuracy of over 95%. Classification of different brands of L‐Histidine using SVM classification. [ABSTRACT FROM AUTHOR]