MOF‐Derived Porous Iron Oxide With Optimized Photothermal and Interfacial Properties for High‐Fidelity LDI‐MS Metabolomics and Digital Traceability of Tobacco Leaves

Engineering inorganic matrices with optimized photon absorption and interfacial energy transfer is pivotal for advancing laser desorption/ionization mass spectrometry (LDI‐MS) toward high‐fidelity metabolomic analysis. Here, we developed a metal‐organic framework (MOF)‐derived porous iron oxide (pFO) nanomatrix for LDI‐MS‐based metabolomics. The pFO exhibits strong UV absorption at 355 nm, a high specific surface area (59.7 m 2 /g), and a uniform mesoporous structure, which collectively afford low background noise, enhanced analyte signal intensity, and high analytical reproducibility (coefficient of variation < 9.2%). Using this platform, metabolic fingerprints were rapidly acquired from 269 tobacco leaf samples. Integrated with Random Forest, the approach achieved accurate discrimination of leaf stalk position (AUC = 0.962) and reliable differentiation between southwestern and southeastern production regions (AUC > 0.944). Furthermore, eight representative m / z features were identified to construct a reduced classification panel, which maintained robust predictive performance in the test set (AUC > 0.913). This study establishes MOF‐derived pFO as a high‐performance inorganic matrix for LDI‐MS metabolomics and establishes a rapid and reproducible analytical strategy for digital classification and quality traceability of agricultural products.