Geographical origin authentication of whole garlic using 16S ribosomal RNA profiling of garlic microbiota and machine learning.

Food fraud through false declarations of geographical origin poses major economic and regulatory challenges, particularly for commodities subject to trade restrictions or premium provenance labeling. Fresh garlic (Allium sativum L.), a globally traded crop, is vulnerable to origin fraud because of long-standing antidumping duties on Chinese imports to the United States. Here, we present a proof-of-concept study demonstrating that microbiota profiling provides a robust and accessible alternative to conventional chemometric approaches for garlic origin authentication. We characterized the surface bacterial communities of 153 garlic samples collected between 2021 and 2024 from China (n = 60), the United States (n = 50), and multiple other countries (n = 43) using 16S rRNA gene amplicon sequencing. Comparative analyses revealed significant differences in alpha and beta diversity across countries, with U.S. samples exhibiting the highest microbial richness and Chinese samples the lowest. Dimensionality reduction methods showed clear clustering by country of origin, supporting the presence of distinct microbial signatures. Machine-learning classifiers trained on 16S profiles achieved >0.87 accuracy across Random Forest, k-nearest neighbors, logistic regression, and support vector machine models using only five genus-level microbial features. Multi-year sampling confirmed that these microbial signals remained stable across harvest seasons. Differential abundance analyses further identified ecologically relevant taxa driving country-level separation. Together, these results establish microbiota profiling as an additional laboratory tool to help investigate garlic origin fraud.