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Development of a 1 H qNMR method for the identification and quantification of monosaccharides in dietary fibre fractions
Development of a 1 H qNMR method for the identification and quantification of monosaccharides in dietary fibre fractions
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Development of a 1 H qNMR method for the identification and quantification of monosaccharides in dietary fibre fractions
Development of a 1 H qNMR method for the identification and quantification of monosaccharides in dietary fibre fractions

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Development of a 1 H qNMR method for the identification and quantification of monosaccharides in dietary fibre fractions
Development of a 1 H qNMR method for the identification and quantification of monosaccharides in dietary fibre fractions
Journal Article

Development of a 1 H qNMR method for the identification and quantification of monosaccharides in dietary fibre fractions

2025
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Overview
Recent research has increasingly focused on the health benefits of dietary fibre (DF), including improved digestion, blood glucose and cholesterol regulation, satiety, and prebiotic effects, which depend on the specific DF type. Traditional gravimetric methods (e.g. Van Soest and AOAC) quantify DF fractions but lack molecular or monosaccharide detail. Advanced chromatographic methods offer more insights but require extensive sample preparation. To address this limitation, the study developed a method using proton Nuclear Magnetic Resonance ( H NMR) spectroscopy to directly analyse DF hydrolysed fractions (mainly pectin, hemicellulose, and cellulose) without the need for derivatisation or neutralisation. It provides detailed structural insights, including the monosaccharide composition, carbohydrate modifications (methylation and acetylation), and the degradation products. The method was validated and then applied to hydrolysed DF fractions obtained from the AOAC process. H NMR shows a comparable monosaccharide distribution to GC-MS, but yields higher recoveries, particularly for uronic acids. In addition, it offers faster sample preparation and acquisition, making it a powerful tool for comprehensive DF analysis.

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