Semantic data integration in flavoromics: linking chemical, analytical, and sensory information

Flavoromics combines broad chemical profiling with multivariate analysis to investigate differences in food flavor associated with product type, processing conditions, and sensory responses.Its analytical scope, however, varies widely across studies, ranging from volatile fingerprinting to integrated analyses incorporating non-volatile metabolites, gas chromatographyolfactometry, odor activity values, sensory evaluation, and machine learning.This variability complicates comparison because detected features, identified compounds, statistical markers, odor-active compounds, and sensory-validated contributors are often interpreted as equivalent findings.This review examines the analytical and sensory evidence used in flavoromics and evaluates how ontologybased knowledge representation may improve data integration.Examples from food, biological, chemical, pharmaceutical, and materials research show that ontologies can distinguish entities, observations, relationships, context, and evidence while preserving provenance.Existing ontologies and databases provide complementary components for flavoromics, but no single resource currently integrates food context, processing conditions, chemical identity, analytical observations, statistical interpretation, and sensory evidence within a unified structure.Linking these resources through clearly defined relationships could facilitate comparison across studies, improve the interpretation of flavor formation, and provide a more reliable basis for marker selection and target-flavor design.