Wine authentication is crucial in combating fraud within the wine industry, which is valued at hundreds of billions of dollars globally. Various fraudulent activities such as dilution, substitution, and mislabeling necessitate the development of robust authentication and traceability techniques.
Spectrofluorometric analysis, coupled with machine learning modeling, has emerged as a promising approach for authenticating wines based on their molecular fingerprints. This application note details the implementation of spectrofluorometric analysis, specifically absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) technique, along with machine learning modeling to trace the molecular fingerprint of wines during the winemaking process and identify the blending percentages of different varietal wines.
A Simple, Fast, “Column Free” Molecular Fingerprinting Technology
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