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This study (open access) compared four different test approaches (DNA barcoding rbcL, DNA barcoding matK , ITS2 barcoding vs the NCBI database, ITS2 barcoding vs the BOLD database) in an authenticity survey of 100 herbal infusions on the Portuguese market.  Samples included 94 single-species products and six polyherbal formulations.

The authors report that DNA extraction was successful for 94 samples, while six single-species products failed to amplify any of the tested barcodes. Among the 88 remaining single-species samples, ITS2 showed the highest amplification success (100 %), outperforming the barcodes rbcL (94 %) and matK (84 %).

Sanger sequencing confirmed the labelled species in 69.3 % of cases with rbcL and 48.9 % with matK. While 63 samples would be considered authentic solely based on barcoding (i.e., if either rbcL or matK matched the label), ITS2 metabarcoding revealed that many of these contained additional undeclared species, indicating that barcoding alone overestimated product authenticity. Of the 85 samples successfully analysed by ITS2 metabarcoding, only 27 (32 %) fully matched their label, while 58 (68 %) contained either additional undeclared species or complete substitutions. Several products contained undeclared species in significant proportions, indicating potential economic adulteration.

The authors conclude that their results revealed (i) the importance of curated and comprehensive databases, with a higher number of species being identified by NCBI database, (ii) the superior sensitivity of ITS2 metabarcoding, and (iii) the widespread mislabelling in commercial herbal products.

Photo by Alice Pasqual on Unsplash

This paper (open access) reports the development of a classification model for the geographic origin on black tea based upon measuring a panel of 15 trace elements by X-ray fluorescence (XRF).  XRF is a non-destructive technique.  The only sample preparation required is grinding the tea leaves into a fine powder.

The model could discriminate between 10 major tea-producing regions.  It was built using reference samples obtained, via tea industry contacts, directly from plantations or primary processing facilities.   791 black tea samples were collected in total: Assam (272 samples), Burundi (40 samples), Darjeeling (145 samples), Ethiopia (40 samples), Keemun (115 samples), Kenya region 1 (41 samples), Kenya region 2 (40 samples), Malawi (40 samples), Rwanda (10 samples), and Sri Lanka (48 samples).

Two unsupervised analysis techniques were used to visualize high-dimensional data, and six supervised models were employed to discriminate the ten GI regions.

The authors report that machine learning models, including random forest, support vector machine, k-nearest neighbours, linear discriminate analysis, and the deep learning multilayer perceptron (MLP) model, demonstrated superior predictive capabilities compared to the traditional partial least squares discriminant analysis model. The MLP model achieved the highest performance, with a 97.7 % overall F1 score in predicting the geographical origins of 532 authentic samples across ten GI regions.

The authors also Identified Rb, Sr, Mn, Si, and Cl as geographical markers for African region discrimination.

The conclude that their work could form the basis and foundation for an international database of tea Geographic Origin, enabling cheap and quick authenticity verification testing.

Photo by Oleg Guijinsky on Unsplash

Flow injection analysis (FIA) is a laboratory-based technique which is faster than liquid chromatography (HPLC) but less selective.

This paper (open access) builds on previous work to use high-throughput FIA-mass spectrometry (FIA-MS) fingerprinting of polyphenols to discriminate chicory from tea of various varieties,  This previous work had failed to discriminate chicory adulteration in the cases of black or green tea.

The authors have repeated the approach but this time using tandem mass spectrometry (MS/MS).  They built a database of the polyphenol fingerprint (55 polyphenols) from 100 tea samples (black, green, oolong, red and white) and from 20 chicory samples.  Database samples were purchased from local markets, so were of unverified source or provenance.  It is likely that this exercise of building the database would need to be repeated with verified samples if the test were to be validated for routine use.  The authors reported excellent chemometric discrimination between the “fingerprints” from tea and chicory, which they were then able to use to detect down to 15% adulteration of tea with chicory.

The authors recommend a workflow where FIA-MS/MS is used as an initial high-throughput screen, with polyphenols in “suspicious” samples then confirmed by LC-MSMS.

For an overview of the difference between MS and MS/MS, see FAN’s analytical techniques explainer pages.

Photo by charlesdeluvio on Unsplash