Microflow LC–IM–QTOF Proteomics for Detecting Nut Adulteration in Walnut Products

Researchers at the University of Erlangen-Nuremberg (Germany) identified the adulteration of walnut products by other edible nuts, specifically cashew, hazelnut, and peanut using bottom-up proteome analysis. The protein extracts of the samples were proteolytically digested and analyzed by micro-flow liquid chromatography ion mobility quadrupole time-of-flight mass spectrometry (microLC–IM–QTOF) with subsequent peptide profiling. A paper based on their work was published in Food Chemistry (1).

Nut material used in food production, such as in spreads, muesli, or as baking ingredients, can fetch high prices in the commodities market (2). Walnuts, known for their high levels of nutritionally valuable lipids and proteins (their first and second largest components, respectively), are open to the potential of food fraud through their deliberate adulteration with low-cost substitutes (3–7).

Guided peptide identification in the non-target LC–MS/MS data was optimized using a custom protein sequence database, leading to the identification of 11 selective marker peptides for the examined matrices in cashew, hazelnut, and peanut. Adulteration levels down to 1% (w/w) could be reliably detected in spiked walnut samples. The authenticity of an independent test set with eight potentially adulterated samples was assessed in comparison to 27 authentic reference samples, achieving 100% accuracy. The acquired non-targeted LC–MS/MS data allow post-hoc re-processing with different protein sequence databases, in case other adulterants should be investigated. (1)

“The presented work,” the authors of the article write (1), “ allows the authentication of walnut against all analyzed adulterants. However, the success of this approach still relies on the coverage of the adulterant species by a protein database and on the availability of protein sequence data. Thus, the application of the method may be limited when nut species are not included in protein databases because they are not studied well and are rarely used in food products. Consequently, these species cannot not be detected by common protein-based authentication approaches. Therefore, a truly non-target authentication method is required, and novel proteomics methods must be developed, in which sample classification is not dependent on database search.”

The authors continue (1) that their method, “could be extended to a quantitative evaluation of the adulteration levels, for example by including data-independent acquisition MS or by the quantification of marker peptides by targeted LC–MS/MS in the multiple reaction monitoring mode (MRM).” MRM analysis, in their opinion, also provides the advantage of being able to detect peptides with a higher sensitivity compared to untargeted analysis. Particularly, low concentrations of adulterations can be detected with a higher reliability. The authors suggest that a switch to LC–MS/MS MRM may also be beneficial in routine control, as the volume of data generated by targeted methods is usually much lower compared to the present untargeted approach. They continue that machine learning algorithms may also be tested for classification of peptides. (1)

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References

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