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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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12992338473?profile=RESIZE_400xThis Masters’ degree project developed a predictive algorithm to categorize butter, butter spreads, and margarine/vegetable oil spreads according to their fatty acid profile, moisture, and total fat content based on the spectra collected by using handheld FT-NIR and portable FT-MIR devices. FT-NIR infrared and FT-MIR performances were similar, with a strong correlation (Rep >0.94) and low standard error of prediction for different analyzed parameters. SIMCA classification model based on FT-NIR and FT-MIR spectra effectively differentiated between butter, butter spread, and margarine/vegetable oil spreads.

The results were benchmarked against “classical” analysis.  Moisture and total fat content were determined using reference methods AOAC 920.116 and AOAC 938.06-1938, respectively. FA profile was determined using Gas Chromatography with flame ionization detector (GC-FID) (AOAC 996.06, 1996.). The FA profile showed that butter-containing products distinguished from margarine/vegetable oil spreads based on the presence of trans fats (TFA) (C18:1t) and butyric acid (C4:0).

The author concludes that portable FT-MIR and handheld FT-NIR technologies offer real-time and in situ analysis capabilities, enabling the dairy industry and regulatory agencies to make actionable decisions regarding FA, moisture, and total fat content and for nutrition, authentication, claims, and labeling purposes of these products.

The abstract and author contact details are available here.  The full text is being withheld until May 2026 at the author’s request. For an overview of FT-NIR see FAN's analytical techniques explainer for spectroscopy.

Photo by Sorin Gheorghita on Unsplash

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