Untargeted Metabolomic Analysis of Honey of Four Botanical Origins Using Conventional and Sugar-Suppressed 1D and 2D NMR Spectroscopy

Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical tool in metabolomics, with applications across biofluids, tissues, plants, and marine samples. It is also vital for evaluating food quality, authenticity, safety, and geographical origin. However, challenges, including low sensitivity, signal overlapping, and the dominance of sugar signals, which obscure primary nonsugar metabolites, limit the broader application in food research. For instance, in honey analysis, conventional 1D 1 H NMR and 2D NMR methods─including 1 H– 1 H J -resolved ( J -RES), total correlation spectroscopy (TOCSY), and 1 H– 13 C heteronuclear single quantum coherence (HSQC)─often suffer from intense sugar signals that mask other vital peaks. To address this, we implemented the suppression of sugar’s moiety signals (SSMS) method alongside conventional 1D and 2D NMR methods. This substantially reduced sugar interference and improved the detection of nonsugar metabolites. This approach allowed for the identification of sixty-eight metabolites, including sugars and nonsugars in honey samples based on four different botanical origins. The suppressed 1D 1 H and 2D NMR methods exhibited a 3-fold increase in the nonsugar signal intensity, improving spectral clarity compared to conventional NMR methods. Combined with multivariate analyses (partial least-squares discriminant analysis and heat maps), this strategy effectively distinguished honey samples based on botanical origins. Additionally, the combined suppression NMR methods enhanced the detection of nutritionally relevant compounds, such as nicotinic acid and trigonelline, underscoring the nutritional value of honey by minimizing carbohydrate signal interference. This advancement improves NMR-based metabolomics in complex food matrixes, enabling more accurate metabolite profiling and quality assessment.