# Spectroscopic Techniques Application for Wine and Wine Byproduct Authentication
## 2 Food Spectral Print Approaches
### Nuclear Magnetic Resonance (NMR)  
NMR spectroscopy is one of the most powerful analytical techniques for obtaining high-throughput spectroscopic and structural information on various molecular compounds. It enables the precise characterization of complex compositional matrices in foodstuffs with minimal sample preparation, allowing for the accurate quantification of selected metabolites within mixtures. As a result, NMR provides comprehensive metabolic profiles that are essential for food authentication [87, 88]. A specialized NMR technique, Site-Specific Natural Isotopic Fractionation (SNIF-NMR), enhances the robustness of molecular fingerprinting. One of its most notable applications is determining the geographical origin of wine, a method officially recognized by the European Union since 1990 ([PERSON], 2001 [89]). SNIF-NMR is widely used to verify the authenticity of various products, including wine and vinegar, by analyzing their metabolic profiles. At its core, NMR spectroscopy detects atomic nuclei with a non-zero magnetic moment, interacting with an external magnetic field. When subjected to an oscillating radio-frequency field, these nuclei emit signals that reflect their chemical environment and interactions with neighboring nuclei, providing valuable insights into their structure. The intensity of these signals correlates with the number of resonating nuclei, making NMR a highly sensitive and reliable analytical tool [90]. One key advantage of NMR spectroscopy is its ability to simultaneously identify and quantify a wide range of chemical compounds, including sugars, organic acids, amino acids, alcohols, and phenolic compounds [91]. The intensity of NMR signals is directly related to the concentration of each compound, a principle that has been validated through international collaborative trials [92]. NMR has been extensively applied in wine research. It has been used to characterize the metabolome of intact grape berries [93], analyze berry extracts to assess maturity levels before harvest [94], and evaluate the influence of terrori [95] and cultivation practices [96] on grape composition. Additionally, metabolic profiling of wines using NMR has facilitated the classification and comparison of wine samples [10]. Since NMR spectra can reveal inherent genotypic variations among grape cultivars, this technique has proven to be a powerful tool for varietal classification [97]. The NMR spectrum of a wine sample serves as a unique molecular fingerprint, making it invaluable for traceability and authentication [19, 98]. Numerous studies have demonstrated its potential for wine authentication, particularly in distinguishing geographical origin [99], vintage [93], and grape variety [100]. In principle, a well-structured dataset of \({}^{1}\)H NMR spectra can simultaneously address multiple authenticity concerns [101, 102]. [PERSON] et al. [103] analyzed approximately 600 German wines and demonstrated that \({}^{1}\)H NMR can generate unique \"fingerprints\" for each wine sample when combined with statistical data analysis. These fingerprints provide valuable insights into various factors, including geographical origin, grape variety, physiological state, vintage, and technological treatments. Additionally, studies have shown that integrating NMR profiling with stable isotope analysis enhances wine authentication, yielding promising results [99]. However, a key challenge in NMR-based wine authentication lies in the multivariate nature of the data. Unlike targeted analysis focusing on individual compounds, NMR evaluates the entire spectral fingerprint using classification models built on reference data. To ensure accurate predictions for unknown samples, these models must be trained on a comprehensive dataset that captures the full spectrum of natural variation. This necessitates the development of extensive reference databases containing spectra from authentic, well-characterized wine samples [104].