Regulatory-based classification of rums: a chemometric and machine learning analysis

Abstract

The Industria Licorera de Caldas (ILC) stands as a major liquor factory in Colombia, specialising in the production of various rum types including Tradicional, Juan de la Cruz, Carta de Oro, and Reserva Especial. These rums, as congeneric drinks, are known for their rich content of volatile compounds that define their sensory characteristics. To be commercialised, each rum batch must comply with Colombian standard NTC278 which defines rigorous assessment of congener content and various physicochemical parameters. Thus, the ILC has accumulated a vast amount of data over the years. This study conducts a comprehensive analysis of ILC rums, using chemometric techniques and machine-learning classification models such as PCA, KNN, LDA, and RF. The aim was to distinguish between rum types based on parameters specified for standard compliance, streamlining the process without the need for additional or extensive new methodologies. As a result, through PCA data exploration, it was revealed that acetaldehyde, ethyl acetate, and isobutanol levels are instrumental in differentiating rum variants. Similarly, all classification models achieved accuracy levels exceeding 0.83 and precision surpassing 0.93. These findings pave the way for further research in the development of an ILC-specific sensor for rapid and reliable liquor authenticity testing.