LAUSR

Abstract Roselle (Hibiscus sabdariffa) quality is strongly influenced by several factors and the geographical origin is one of the key parameters. However, fraudulent practices including mislabeling of the geographical sources might occur. In the present experiment, the analyzed samples consisted of 64 authentic samples originating from the world's best roselle country (Sudan) and eight samples from the world's largest producer (China) were investigated. The study investigated whether near-infrared spectroscopy (NIR), low filed NMR (LF-NMR) spectroscopy and fluorescence spectroscopy can enable roselle geographical origin to be identified. Principal components analysis (PCA), hierarchical cluster analysis (HCA) and PCA combined with linear discriminant analysis (PCA-LDA) were performed on NIR data to assess a possible classification of samples based on origin. Roselle samples from the same geographical areas might group together in the PCA plot. Correct discrimination was achieved by HCA. The classification of the samples into calibration and prediction sets yielded 100% discrimination rates for both calibration and prediction sets. LF-NMR measurement, to detect differences in the relaxation times, indicated that these were affected by the variations in geographical origins. Additionally, the fluorescence spectroscopy spectra presented different shapes and intensity of fluorescence emissions, demonstrating the differences in the samples. This study proved that the three spectroscopies could be viable tools for utilization in classifying roselle samples by their geographical origins.