Comprehensive chemical fingerprinting by LC×LC-fluorescence and data-driven chemometric modelling for unsupervised...

This study presents, for the first time, the development and application of comprehensive two-dimensional liquid chromatography (LC × LC) with absorbance and fluorescence detection, coupled with chemometrics. A rigorous, methodologically optimised LC × LC protocol was established to enhance resolution and analytical performance for food profiling, using Spanish cavas as a proof-of-concept. Once optimised, a data-driven exploratory analysis evaluated whether LC × LC profiles could serve as reliable chemical fingerprints for sample characterisation. A streamlined strategy was implemented, in which PARAFAC scores were directly extracted from LC × LC data and utilised for unsupervised analyses, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA). Fluorescence detection provided deeper chemical insight than UV alone, revealing clear clustering patterns associated with geographical origin, composition, and producer-related features. Integration of UV and fluorescence data through mid-level data fusion further enhanced these distinctions, underscoring the value of combining complementary detection modes. This work demonstrates that integrating advanced LC × LC platforms with data-driven chemometrics offers a practical, non-targeted approach to food profiling, providing meaningful chemical differentiation and a powerful alternative to conventional LC × LC workflows.