Progenesis QI turns food standards detective

Basmati rice being rinsed before boilingAt Nonlinear, we always like to see our software being applied to new scientific areas – it feels very much like we’re on the right track when that happens! With that in mind, I’d like to highlight a new involvement in food forensics: adulteration testing of Basmati rice.

Basmati is an aromatic, high quality rice grown only in certain regions of India and Pakistan but is often mixed with lower quality rice. In the UK, for example, the maximum non-Basmati content before rice must be labelled a mixture is 7%, but 16% of samples labelled as Basmati rice, assessed as recently as 2009/10, were in violation of this standard [1].

Currently, adulteration testing methods focus on DNA microsatellite analysis [2], but our colleagues at Waters have been bringing mass spectrometry and the multivariate statistical visualisations in Progenesis QI to bear on the problem, with a view to developing novel and complementary approaches.

As a proof-of-principle study, Cleland et al. [3] studied off-the-shelf rice samples using Atmospheric Pressure Gas Chromatography (AP-GC) followed by HD-MSE analysis with a SYNAPT G2-Si mass spectrometer. The samples included Basmati rice from four manufacturers, and one long grain and two Jasmine rice samples as comparators. The data generated were analysed in Progenesis QI with supplementary visualisations in EZinfo (Umetrics).

One of the strengths of metabolomics and subsequent multivariate statistical analysis is that a huge range of data points can be simultaneously quantitated, and samples classified sensitively based on correlated differences arising across the data set. Naturally, our no-missing-values approach helps to ensure the conclusions reached are statistically robust! In this study, 3885 compound ions were detected using Progenesis QI; when the data were plotted using PCA, two conclusions were easily reached (Figure 1).

Figure 1 Figure 1. Separation of rice by type and manufacturer. Note that of the four Basmati samples, one clusters with Jasmine rice (behaving similarly to the Jasmine rice from the same manufacturer) and one with Long Grain rice. Reproduced from [3].

Firstly, the method appears reproducible in terms of inter- versus intra-sample variation. Secondly – and very interestingly given the purpose of the study – some of the Basmati rice samples cluster very distinctively with Jasmine and Long Grain samples, potentially with implications about the provenance or purity of those samples. It should be noted that this is a proof-of-principle study only, so no definitive conclusions on causation can be drawn, but it seems that the methodology can distinguish different grains effectively.

Naturally, to function as a test for adulteration on an ongoing basis, it would be important to identify the species driving this separation such that appropriate targeted assays can then be designed. Towards this, Progenesis QI and EZinfo also allowed the application of OPLS-DA (Orthogonal Projection to Latent Structures Discriminant Analysis) to determine the compounds most associated with the sample clustering discrimination, and the visualisation of their abundance profiles across the groups (Figure 2). Potential markers could then be provisionally identified through database searching.

Figure2 Figure 2. A cluster of potential markers elevated in Basmati rice relative to other grains derived from Progenesis QI analysis. (A) and (C) show the cluster in hierarchical dendrogram and abundance profile views. Reproduced from [3].

While it is early days as yet, this proof-of-principle study raises the prospect of novel tests for rice adulteration, which might increase the accuracy and confidence of its detection. Further work would initially focus on validation of the results using a larger, well-characterised sample set.

Want to know more?

If you are interested in learning more about this study, there are two ways you can do this. The application note itself is available for downloading; but there is also an opportunity to hear from the author directly when Gareth Cleland delivers a webinar on the 9th of December on this work.

References

  1. Food Standards Agency, UK. “UK Local Authorities Imported Food and Feed Sampling Report 2009/10.
  2. Nader W.F., Brendel T., Schubbert R. (2013) “DNA-analysis: enhancing the control of food authenticity through emerging technologies.” Agro FOOD Industry Hi Tech 24(1): 42-46.
  3. Cleland G., Ladak A., Lai S. and Burgess J. (2014) “The Use of HRMS and Statistical Analysis in the Investigation of Basmati Rice Authenticity and Potential Food Fraud.” Waters application note, part number 720005218en.

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