Fingerprinting methods for origin and variety assessment of rice: development, validation and data fusion experiments

Several analytical techniques, i.e. Near Infrared (NIR) and Mid-Infrared (MIR) spectroscopy, Hyper Spectral Imaging (HSI), Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Proton-transfer Reaction Time-of-Flight Mass spectrometry (PTR-TOF-MS), combined with chemometrics, are examined to evaluate their potential for solving food authenticity questions on the case of rice. In total, 237 rice samples were analyzed in this study to examine origin and variety assessment and sourced from producing countries (Italy, Spain, Vietnam, Pakistan and Thailand). The Gaussian Process Latent Variable Model (GP-LVM) was applied as technique to obtain a meaningful compressed representation of the data in a two-dimensional space followed by a classification with a nearest neighbour algorithm. What concerns the origin assessment, GC-MS results score good across all countries, resulting in the most accurate method, with prediction rates ranging from 86% to 94%. Data fusion experiments of combination of GC-MS with NIR, or GC-MS with HSI resulted in prediction rates on origin assessment of more than 90%. Variety assessment was performed with these analytical techniques. Using single techniques, HSI achieved prediction values above 90% for all classes (96%–99%). For data fusion experiments of variety assessment combining GC-MS and NIR all prediction values were equal or higher than 92%; for GC-MS combined with HSI, prediction values were all 98%. The presented methodology can be successfully used for the authentication of rice.