Hierarchical flower-like Au/trimetallic oxide heterostructures for LDI-MS metabolic fingerprinting toward livestock health surveillance and food chain safety

Au/TOHFHs enable reproducible LDI-MS metabolic fingerprinting of cattle. • Serum metabolic signatures reveal zoonotic infection-associated changes. • Machine learning accurately classifies BTB and BRU from healthy cattle. • The strategy supports livestock surveillance and food safety monitoring. Rapid analytical methods capable of discriminating livestock diseases are highly desirable for early animal health surveillance and food-chain risk control. Herein, a gold nanoparticle-modified ternary metal oxide hierarchical flower-like heterostructures (Au/TOHFHs) was developed as a high-performance matrix for laser desorption/ionization mass spectrometry (LDI-MS)-based metabolic fingerprinting. The hierarchical architecture and plasmon-enhanced interface synergistically improved metabolite enrichment, energy utilization, and ion efficiency. Using standard metabolite solutions, the Au/TOHFHs matrix exhibited good analytical reproducibility, with coefficients of variation of 5.24−14.95% for intra-spot replicate measurements and 6.71−14.90% for inter-spot spatial sampling. Using 178 bovine serum samples, including 96 samples from cattle with bovine tuberculosis, 31 from cattle with brucellosis, and 51 healthy controls, the platform accurately distinguished bovine tuberculosis and brucellosis from healthy cattle, achieving receiver operating characteristic curve areas under the curve of 0.983 and 0.899, respectively, in independent validation. Furthermore, simplified nine-metabolite and seven-metabolite biomarker panels for bovine tuberculosis and brucellosis, respectively, retained robust predictive performance, with independent-validation areas under the curve of 0.983 and 0.958, respectively, while reducing model complexity. This strategy enables rapid, label-free, and high-throughput livestock screening from minimal serum volumes, providing a practical tool for large-scale herd surveillance and strengthening food safety management through source-level risk control. Download: Download high-res image (295KB) Download: Download full-size image