Classification of Brandy and Cognac Production by Geographical Origin and Aging Using Raman Scattering and Machine Learning

The questions of developing simple and accessible methods for monitoring the authenticity and quality of alcohol production based on Raman scattering are examined. The most important characteristics of brandy and cognacs, their geographic origin and aging period, which substantially determine the cost of production and are often objects of falsification, are studied. Samples of cognac production are classified in terms of geographic origin and aging by means of combination (Raman) spectroscopy with spatial mixing, which makes it possible to check the authenticity and quality of the alcohol production. This method has the advantages of simplicity of sample preparation up to its complete absence, high selectivity, rapidity and simplicity of analysis. This method can be used for developing compact instruments that make it possible to carry out an analysis directly at the sampling site. The origin and aging period are measured. Raman scattering spectra of 42 different samples of brandy and cognacs with different geographic origins and aging periods were measured. It is shown that fragments of spectra measured in the the range of Raman shifts of 800–3000 cm^–1 are the most informative for solving the problems posed here. Learning and test samples were set up from the samples studied here. Models learned with the aid of an algorithm for extreme gradient boosting are used for processing the data. The correctness of recognition in terms of geographic origin and aging period for undiluted samples from the test set, spectra from which were not used for learning the model, was 100%. The results of this study can be used for express monitoring of the authenticity of alcohol production and determining its characteristics using Raman scattering spectra and their further processing by machine learning methods.