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Multimodal Spectral Fusion for Food Authenticity and Geographical Origin Traceability: Principles; Modeling Strategies; and Application Advances
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DOI：10.1039/D6AY00650G.png)
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Abstract
En 中文
Food authenticity and geographical origin traceability now affect safety governance; recall management; market surveillance and consumer trust. For high-value foods; mislabeling and adulteration are rarely controlled by one marker; they reflect coupled changes in molecular composition; elemental background; spatial heterogeneity; processing history and supply-chain context. This critical review therefore asks when multimodal spectral fusion adds independent analytical evidence; rather than merely enlarging the variable space. We evaluate this question through physicochemical complementarity; blockmatrix formalism; leakage-controlled validation; quantitative performance audits and routine-laboratory constraints. The resulting rule is conditional. Low-level fusion is defensible only for commensurate; strongly preprocessed and transparently weighted blocks with sufficient independent samples. Mid-level fusion is the most reliable default for the small-to-moderate food-authenticity datasets that dominate the field; because it controls redundancy while preserving cross-block interactions. High-level fusion is mainly an operational strategy for asynchronous instruments; missing blocks; confidence-based rejection or fault-tolerant screening. Latent deep fusion should be reserved for large; balanced; blocked and externally tested datasets. Reported examples show that fusion is most persuasive when it improves the same-task single-modality baseline under independent or external validation; as in milk-type classification; salmon authentication and saffron origin studies; however; the salmon retail-set gain is suggestive rather than definitive because the external set is small. Conversely; small random-split improvements are not evidence of a chemometric breakthrough. Future progress depends less on stacking sensors than on defining minimal effective modality combinations; reporting uncertainty; confronting software and graphical-user-interface limitations; validating across batches; years and instruments; and justifying the cost; throughput; maintenance and training overhead of multi-instrument workflows.
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