From Chemical Profiling to Algorithmic Authentication: Micro-Scale Fruit Origin Discrimination in Modern European Agricultural Systems

The geographical authentication of apples based on mineral profiles is complicated by substantial variability within and between production regions. This study examined the impact of geographical class definition and dataset structure on the classification of apple origin using linear discriminant analysis (LDA), and whether these effects could support a stepwise classification strategy. The mineral and isotopic profiles of four apple cultivars (‘Gala’, ‘Golden Delicious’, ‘Jonagold’, and ‘Idared’) originating from the Czech Republic and Poland were evaluated using five LDA models that differed in terms of geographical class definition, dataset size, and macro- versus micro-regional grouping. Exploratory analyses identified region-associated variation in twelve elemental and isotopic variables. Model performance was strongly affected by dataset structure and geographical class definition. Decomposing macro-regions into micro-regions did not universally improve classification, but it may reveal substantial intra-regional heterogeneity and the identification of specific collision regions that can be responsible for a considerable proportion of the classification uncertainty. Based on these findings, a conceptual hierarchical multiple-model framework is proposed, in which a broad reference model would provide an initial classification and samples assigned to collision regions are subsequently evaluated using dedicated local models. This strategy may represent a useful component of a broader approach to geographical authentication based on mineral profiles.