# 1H-NMR Analysis of Wine Metabolites: Method Development and Validation

## TL;DR

This paper develops and validates a fast, accurate 1H-NMR method for wine metabolite analysis to combat counterfeiting and improve quality control.

## Contribution

A standardized, automated 1H-NMR workflow is developed and validated for quantifying 20 wine metabolites with high precision and minimal bias.

## Key findings

- The method demonstrated excellent linearity, trueness, and reproducibility under OIV standards.
- Measurement uncertainties were estimated using both conventional and dynamic approaches.
- The workflow is easy to implement, minimizes sample consumption, and reduces operator bias.

## Abstract

Wine, as a high-value product, is vulnerable to counterfeiting. To tackle increasingly sophisticated fraud, innovative analytical approaches are required. However, they must undergo rigorous validation. Proton nuclear magnetic resonance spectroscopy (1H-NMR) is intrinsically quantitative, reproducible, and fast, making it a promising tool for official control. This study presents the development and validation of a standardised and fully automated workflow for the quantification of 20 oenologically relevant compounds, including organic acids, sugars, alcohols, esters, phenolics, and an alkaloid. The method combines optimised sample preparation, external quantification standards, spectrometer calibration, and a dedicated R package (RnmrQuant1D) for fully automated spectral processing, enabling high-throughput and operator-independent analysis. Validation was performed under intermediate precision according to OIV metrological standards, evaluating accuracy, precision, robustness, limits of quantification, and measurement uncertainty. The results demonstrated excellent linearity, trueness, and reproducibility, matching the targeted analytical performance. Measurement uncertainties were estimated both by conventional linear modelling and by a dynamic approach better suited to detection limits. The workflow is easy to implement, requires minimal sample consumption, and substantially reduces operator bias. Beyond validating a robust method, this study provides a framework for harmonised, transferable 1H-NMR workflows that could support large-scale databases, integration with chemometric models, and ultimately, 1H-NMR’s recognition as a relevant method for wine authentication and quality control. This work fills a crucial gap in wine analysis by uniting practical application and rigorous methods, enabling broader adoption in control laboratories worldwide.