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Purpose This study examines the emerging research frontier of Food Analytics between 2016 and mid-2026. It identifies descriptors that show sustained novelty and growth, analyses how they combine into emergent thematic clusters, and synthesizes these clusters into broader research dimensions that shape the field's current research frontier. Design/methodology/approach The study adopts a technological-intelligence design based on bibliometric tech mining and analyzes 1,967 Scopus-indexed publications using descriptor normalization, Porter's emergence indicators, document-level co-word analysis, and Louvain community detection. Emergent descriptors were identified using temporal growth and novelty criteria, clustered by co-occurrence, and interpreted comparatively to derive higher-order analytical dimensions. Findings The analysis identified 111 emergent descriptors organized into five Louvain communities after a sensitivity analysis of the emergence-ratio threshold and evaluation of cluster coverage and interpretability. The five-community solution was synthesized into four substantive dimensions: intelligent food quality and composition analytics; predictive and operational food-system analytics; personalized nutrition and consumer analytics; and semantic, generative, and knowledge-driven food analytics. The difference between the five communities and four dimensions reflects the integration of a predominantly methodological community across the substantive dimensions in which its methods are applied. Originality/value The study moves beyond technology-specific reviews by identifying emergence at the descriptor level and integrating overlapping clusters into a coherent framework of research dimensions. It provides a forward-looking account of how Food Analytics is expanding and clarifies the technological, methodological, and application areas most likely to shape its next stage of development.
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Velasquez et al. (2026) studied this question.
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