How climate change can help fighting fraudulent declaration of cereal grains

Stefan Bindereif1, Lucas Köberle2, Peter Kolb3, Stephan Schwarzinger3, Gerhard Gebauer1
1 BayCEER – Laboratory of Isotope Biogeochemistry, 95447 Bayreuth, Germany
2 ALNuMed GmbH, 95448 Bayreuth, Germany
3 Northern Bavarian NMR Centre, 95447 Bayreuth, Germany

O 2.1 in Stable isotopes in environmental research - bridging disciplines, organisms and processes

10.10.2019, 14:30-14:45, H36, NW III

Introduction

The year 2018 was one of the driest and warmest since weather records began in 1881. Farmers had to cope with heavy losses in both crop yield and quality. To maintain quality parameters, traders are forced to mix batches from several harvest years, which must be declared. Customers insist on buying cereal grains from a single harvest year and pay less for mixed batches. Combined with the competitiveness of the market, the risk of mislabelling increases. Isotope-ratio mass spectrometry (IRMS) could tackle this problem as plant-based products reflect characteristics of their environment and physiology through their isotope signatures, hence also inter-annual changes in e.g. temperature or amount of precipitation.

Material and Methods

Here, we analysed 406 cereal grain samples from Germany (barley: 219, spelt: 187) from the harvest years 2016-2018 for their C, N and O isotope data as well as their element concentrations. Further, we applied proton nuclear magnetic resonance (1H NMR) to substantiate our findings and near-infrared spectroscopy (NIRS), a technique with potential to be used online. Special attention was paid during sample selection and data evaluation in order to consider and minimize the variability introduced by factors such as genotype or region.

Results

Both IRMS and NIRS showed clear trends for discrimination between harvest years, especially for the year 2018. Separation of the groups was significantly improved when using combined data sets and multivariate statistical approaches. Carbon and oxygen natural isotope abundances were the most important variables included into the model. Furthermore, 1H NMR fingerprints indicated severe drought stress in samples from 2018 with elevated levels of marker substances.

Conclusions

We are convinced that due to the increased frequency of extreme weather events, this multi-method approach turns out in the future as a valuable and urgently required tool in fighting fraudulent declaration of cereal harvest years.

 




Keywords: cereal grains, drought, harvest year, food fraud, IRMS, NIRS, NMR

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