bacteria (2)

31091284482?profile=RESIZE_400xVerifying the origin of garlic has risen up the risk rankings in recent years.  Approximately 70% of the world’s garlic originates from China.  Volatility in trade tariffs (and the anticipation of tariffs) and anti-dumping measures mean that there could be financial incentive to trans-ship Chinese garlic through a third country and mis-state the country of origin, particularly if importing into the US.

In this proof of concept study (USD32 download fee) the authors show that microbiota profiling provides an alternative to conventional chemometric approaches for garlic origin authentication. They 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.

They report that 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 neighbours, 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.

Photo by team voyas on Unsplash

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13668929460?profile=RESIZE_400xThe authors of this study (open access) used the results and datasets from 18 published projects and biobanks to build a database of bacterial metataxonomic data from fermented table olives.  The collated database contained database 442 samples of 16S rRNA bacterial profiles

They then compared three tree-based Machine Learning algorithms—Classification and Regression Tree, Random Forest (RF), and Extreme Gradient Boosting— to classify the origin or production process of the olives. They report that Machine Learning techniques can effectively classify bacterial profiles based on olive processing type, cultivar, country of origin, and isolation matrix. The Random Forest model achieved the highest accuracy, reaching 97% in the best cases, with a kappa coefficient above 0.8 for most categories.

They conclude that approach holds potential applications in the table olive sector and in other food products, where the industrial application of ML techniques to metataxonomic data could enhance traceability, authenticity, and quality control.

Photo by Melina Kiefer on Unsplash

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