Abstract
Safety along the food and feed supply chain is an emerging topic and closely linked to the ability to analytical trace the geographical origin of food or feed. In this study, ultra-performance liquid chromatography coupled with electrospray ionization quadrupole-time-of-flight mass spectrometry was used to trace back the geographical origin of 151 grain maize (Zea mays L.) samples from seven countries using a high resolution non-targeted metabolomics approach. Multivariate data analysis and univariate statistics were used to identify promising marker features related to geographical origin. Classification using only 20 selected markers with the Random Forest algorithm led to 90.5% correctly classified samples with 100 times repeated 10-fold cross-validation. The selected markers were assigned to the class of triglycerides, diglycerides and phospholipids. The marker set was further evaluated for its ability to separate between one sample class and the rest of the dataset, yielding accuracies above 89%. This demonstrates the high potential of the non-polar metabolome to authenticate the geographic origin of grain maize samples. Furthermore, this suggests that focusing on only a few lipids with high potential for grain maize authentication could be a promising approach for later transfer of the method to routine analysis.
Highlights
Published: 13 September 2021The traceability of goods, especially food and feed, is an emerging topic, as supply chains are more and more complex in a globalized market
151 individual grain maize samples from seven countries (14–25 samples/country) worldwide were measured by liquid chromatography coupled with mass spectrometry (LC-MS) to establish a model for analytical geographic origin determination of commonly traded grain maize
The ability of analytical geographical origin verification based on the non-polar metabolome of grain maize samples by classification was investigated
Summary
The traceability of goods, especially food and feed, is an emerging topic, as supply chains are more and more complex in a globalized market. Today the verification is mainly done by checking shipping documents, which may be falsified. Methods such as the blockchain strategy, which in principle make it possible to trace the entire supply chain back to its origin, are being developed, but are not immune to manipulation from the real world (from the field) into the digital world (the blockchain). The only way to verify this is by using experimental analytical methods [1]. There is a high demand for analytical methods to objectively verify the origin of feed or food samples [2]
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