# Spectroscopic Techniques Application for Wine and Wine Byproduct Authentication
## 4 Emerging Trends in Wine and Byproduct Authentication  
Ensuring the authenticity of the wine and its byproducts is crucial to maintaining consumer confidence, protecting brand reputation, and meeting regulatory standards. This is a widely debated topic among experts, especially given the increasing internationalization of wine and its byproducts [141]. The methods described in this manuscript offer significant advantages, such as rapid results acquisition, minimal sample preparation, and the ability to perform non-destructive analysis [4]  
In addition, DNA-based methods have gained prominence in wine authentication [25]. Wine DNA authentication involves verifying the authenticity of wines by identifying the genetic material of the grape varieties used. This process includes various techniques for extracting and analyzing DNA from wine samples to confirm the wine's varietal composition and authenticity. The first step is DNA extraction and sample preparation. DNA is extracted from wine debris using methods such as precipitation with polyvinylpyrrolidone and dimethyl sulfoxide (DMSO) lysis, which help reduce contamination risk [142]. However, enologists' filtration treatments can significantly reduce DNA content, affecting traceability, as different filter materials impact DNA recovery rates [143]. After DNA extraction, the method for DNA analysis must be chosen. Microsatellite markers are used to analyze DNA from must and wine, allowing the identification of grape cultivars. This method is effective during fermentation but becomes less reliable in finished wines due to DNA degradation [144]. Single-nucleotide polymorphism (SNP) genotyping assays, such as TaqMan, are used for varietal authentication; however, DNA loss during winemaking can limit their effectiveness [145]. Alternatively, a quartz crystal microbalance (QCM) biosensor, or QCM-DNA biosensor, can detect SNPs without PCR, offering a more straightforward approach to assessing wine authenticity [146]. Both optical and QCM-DNA biosensors have been developed to detect DNA in complex matrices, such as wine, offering a label-free method for varietal identification [147; 146]. Wine DNA fingerprinting can also be achieved by using Microsatellite DNA Analysis (SSR) combined with bioinformatics tools to determine the varietal composition of wines, even in blends, by comparing DNA profiles to known grape variety candidates [148]. This method is used to identify grape varieties [149]. However, it has some limitations. According to [PERSON] [150], DNA degradation over time makes it increasingly tricky to obtain reliable samples, particularly in older wines. Techniques such as real-time PCR and next-generation sequencing (NGS) are used to identify and differentiate grape species and varieties and detect adulteration. These methods offer high specificity and sensitivity and are widely used in the food industry to ensure product authenticity [151]. Wine DNA authentication employs various techniques, including DNA extraction, microsatellite markers, SNP detection, and biosensors, to verify the authenticity of wines. These methods help to ensure the accurate identification of varietal composition and maintain the quality of wine. However, challenges such as DNA degradation during winemaking and the effects of the filtration process remain. Advances in biosensor technology and bioinformatics are increasingly enhancing the accuracy and applicability of these techniques within the wine industry. Artificial intelligence (AI) has revolutionized wine authentication, providing new methods for analyzing and interpreting complex data. Machine-learning (ML) algorithms are trained to analyze the chemical composition of wines, enabling fraud detection with high accuracy [152]. [PERSON] et al. [153] study highlights experimental data obtained through \({}^{1}\)H-NMR spectroscopy, combined with AI models, to detect unique signatures associated with different regions, grape varieties, and harvest years.