Slim chance of purity with internet herbal weight loss products

Skip to Navigation


  • Published: May 15, 2017
  • Author: Ryan De Vooght-Johnson
  • Channels: Laboratory Informatics / Chemometrics & Informatics
thumbnail image: Slim chance of purity with internet herbal weight loss products

Internet herbal slimming aids often contain banned substances

There are a vast number of ‘herbal’ slimming aids available over the internet, typically backed by glowing testimonials. However, in practice many of these only have any efficacy because they are adulterated with banned slimming drugs or other noxious chemicals. An example is the slimming drug sibutramine, an amphetamine analogue that was withdrawn in most countries due to cardiovascular risks. Another common adulterant is the laboratory indicator phenolphthalein, which is a strong laxative, not normally used in medical practice owing to concerns over its possible carcinogenicity. Caffeine is also found in many ‘herbal’ slimming aids.

The large number of potential contaminants and the compounds from the herbs give complicated chromatograms. The Brussels researchers used UHPLC, together with a number of chemometric techniques, to classify seized ‘herbal’ slimming aids into distinct classes. It was hoped that such a classification would enable the health risks of any newly seized material to be rapidly assessed, and would also help to identify the potential sources of these dangerous mixtures.

Adulterated slimming aids examined by UHPLC, HCA, CART and PLS-DA

92 samples seized by customs were examined by UHPLC, along with reference standards from 12 common adulterants. The seized samples were typically extracted with 50% aqueous methanol under sonication, followed by the removal of solids by centrifugation.

Firstly, the samples were analysed by UHPLC using a PDA detector (photodiode array detector, i.e. a diode array detector, DAD). The samples were analysed using a Waters Acquity™ Ultra Performance LC system with an Alltech VisionHT C18-P column. Gradient elution was carried out with methanol and aqueous ammonium acetate (0.02 M, pH 5). The peaks were aligned using the ‘COW’ algorithm (correlation optimised warping). Secondly, all the samples were analysed by LC-MS, using the same column connected to a Waters Synapt-G2S ToF mass spectrometer with an electrospray source in positive mode, with the buffer being altered to ammonium formate (0.01 M, pH 5). Mass spectrometry was used to give data in the form of TICs (total ion chromatograms) and also in the form of the intensities of the compounds’ m/z values (referred to as the ‘MS fingerprint’). Sibutramine, phenolphthalein or caffeine were present in many of the samples.

Hierarchical cluster analysis (HCA) was carried out on the HPLC PDA data, which were classified into six clusters. Ideally, each cluster should be composed of samples with similar ingredients; this was the case for three of the six clusters For example, all 25 samples in cluster two contained sibutramine (three of them also contained phenolphthalein). Two of the clusters showed some internal consistency, while the remaining cluster was composed of samples with little similarity between them (an ‘odds and ends’ cluster). It was found that running HCA on the LC-MS data did not give any clear clusters.

PLS-DA was used to construct models, using some of the data as a training set and the remainder as a test set. Based on the PDA analysis fingerprints, 85% of the samples in the test set were assigned to the correct cluster. Based on the TIC analysis, 80% were assigned correctly, but the figure was only 55% based on the MS fingerprints.

CART analysis was also used as a classification method, constructing ‘trees' from the data. Again, training and test sets were used. Correct classification was obtained in 75% of cases with PDA, 70% for TIC and 65% for MS fingerprints.

Chemometrics can help classify adulterated herbal remedies

The chemometric methods helped to classify complex data covering many different samples. The techniques employed could be extended to other samples containing different adulterants. The widespread detection of harmful substances in ‘herbal’ remedies should be a timely warning to anybody considering purchasing these from potentially unreliable sources.

Related Links

Drug Testing and Analysis, 2017, Early View paper. Custers et al. Clustering and diagnostic modelling of slimming aids based on chromatographic and mass spectrometric fingerprints.

Wikipedia, Decision Tree Learning

Wikipedia, Hierarchical Clustering

Article by Ryan De Vooght-Johnson

The views represented in this article are solely those of the author and do not necessarily represent those of John Wiley and Sons, Ltd.

Follow us on Twitter!

Social Links

Share This Links

Bookmark and Share


Suppliers Selection
Societies Selection

Banner Ad

Click here to see
all job opportunities

Most Viewed

Copyright Information

Interested in separation science? Visit our sister site

Copyright © 2020 John Wiley & Sons, Inc. All Rights Reserved