Additionally, the fluorescence spectroscopy spectra presented different shapes and intensity of fluorescence emissions, demonstrating the differences in the samples. LF-NMR measurement, to detect differences in the relaxation times, indicated that these were affected by the variations in geographical origins. The classification of the samples into calibration and prediction sets yielded 100% discrimination rates for both calibration and prediction sets. Correct discrimination was achieved by HCA. Roselle samples from the same geographical areas might group together in the PCA plot. 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. 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. 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. However, fraudulent practices including mislabeling of the geographical sources might occur. Roselle (Hibiscus sabdariffa) quality is strongly influenced by several factors and the geographical origin is one of the key parameters.
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