HR‐1H NMR spectroscopy and multivariate statistical analysis to determine the composition of herbal mixtures for infusions. Issue 4 (14th October 2020) - Marchetti, Lucia; Rossi, Maria Cecilia; Pellati, Federica; Benvenuti, Stefania; Bertelli, Davide
Abstract: Introduction: The ever‐growing diffusion and consumption of herbal teas, due to their sensory attributes and well‐known health benefits exposes them to the real risk of adulteration, especially in the case of commercial mixtures already minced for infusion. Therefore, novel and suitable tools for the control of these valuable products are increasingly required. Objectives: This work provides new insights for the authenticity study of infusions. The main objective was verifying the potential of proton nuclear magnetic resonance ( 1 H‐NMR) combined with partial least square (PLS) regression to build highly predictive models, useful for the determination of the real amounts of herbs in mixtures, by the simple analysis of the related infusion. Materials and methods: Peppermint, fennel, lemon balm, and passiflora were chosen to set‐up an experimental plan according to a central composite design (CCD). One‐dimensional nuclear Overhauser effect spectroscopy (1D‐NOESY) spectra were properly pretreated and then analysed by chemometrics to extract significant information from the raw data. Results: Venetian‐blind cross‐validation and different chemometric indicators (RMSEC, RMSECV, RMSEP, R 2 CAL, R 2 CV, R 2 PRED ) were used to establish the best model, which include four factors explaining 88.70 and 83.77% of the total variance in X and Y, respectively. Conclusions: These promising results have laid the basis for further development of the method, to extend its applicability and make it more scalable. This tool could replace expensive separative techniques and protect the rights of consumers with particular attention to safety issues and quality assurance. Abstract : This study aims to verify the potential of proton nuclear magnetic resonance ( 1 H‐NMR) combined with partial least square regression to determine the composition of herbal mixtures. NMR one‐dimensional nuclear Overhauser effect spectroscopy (1D‐NOESY) spectra were analysed to extract significant information from the raw data. The best performing model included four factors explaining 88.70 and 83.77% of the total variance in X and Y, respectively. These promising results have laid the basis for further development of the method, useful also for the analysis of commercial herbal infusions.