High precision quantification of Betaine in cereal-based matrices by 1H NMR

Betaine is a key osmoprotectant involved in plant stress responses and a potential biomarker for cultivar differentiation. Its quantification in cereal-based matrices is analytically challenging due to high polarity and spectral overlap. This study presents a semiquantitative 1H NMR protocol developed and validated through an extensive interlaboratory comparison involving 50 spectrometers across 12 countries. The method was applied to aqueous extracts of two durum wheat cultivars (cv. Marco Aurelio and Iride) and their corresponding pasta products. Three calibration strategies (external standards: TSP and DMSO2) and betaine self-reference were compared to ascertain the most precise and accurate method. This was achieved using a semi-quantitative approach. The Horwitz ratio was used to evaluate reproducibility, with values lower than 2.0, thereby substantiating the robustness of the approach [1]. Quantitative nuclear magnetic resonance (qNMR) spectroscopy is a powerful tool for chemical analysis. The semi-quantitative protocol does not require calibration curves, thus facilitating direct quantification with minimal sample preparation. However, it is important to note that the accuracy and reproducibility of these measurements can be affected by various factors when different spectrometers and operators are involved. This study emphasises the importance of standardised correction strategies and harmonised data processing protocols, demonstrating that highly reproducible quantification methods based on proton nuclear magnetic resonance (1H NMR) can be achieved without the need for traditional calibration curves. The study's results show that semi-quantitative 1H NMR spectroscopy can provide reliable data for metabolite profiling, even in complex food matrices. This development presents novel opportunities to enhance traceability, quality control and authenticity verification within agri-food systems, particularly in the context of pressures from climate change. This research paves the way for the application of qNMR to a wider range of complex matrices that have traditionally been difficult or costly to analyse using other methods. These fields include nutraceuticals, pharmaceuticals, agriculture, and environmental sciences [2].