Normalization of LC-MS mycotoxin determination using the N-alkylpyridinium-3-sulfonates (NAPS) retention index system


Renaud, J.B., Hoogstra, S., Quilliam, M.A., Sumarah, M.W. (2021). Normalization of LC-MS mycotoxin determination using the N-alkylpyridinium-3-sulfonates (NAPS) retention index system. Journal of Chromatography A, [online] 1639

Plain language summary

Comparison of data between laboratories is one of the great challenges for all analysis but is especially difficult for the food safety problem of mycotoxins. In this study we introduced the use of a set of chemicals with every analysis. These chemicals don't affect the analysis but allow for standardization of data. Their use makes it easier for labs to identify unknown compounds in food and feed samples helping to ensure the safety of our Agri-Food system.


A major challenge for LC-MS analysis is the ability to compare data between laboratories and across instrument platforms. Currently, mycotoxin determination relies on dereplication strategies based on physicochemical properties such as the m/z of the precursor and product ions. Unlike these intrinsic properties, retention time (tR) is an extrinsic property impacted by LC conditions, including mobile phases and column chemistry, making exchange of data between groups difficult. To address this, we are introducing the concept of incorporating an electrospray compatible, retention index (RI) system based on a series of N-alkylpyridinium-3-sulfonates (NAPS) into routine mycotoxin determination. These standards of differing alkyl chain length span RI units from 100 to 2000, are UV active and have fixed positive and negative charges for electrospray ionization in either mode. Using high resolution LC-MS/MS, the RIs of 96 mycotoxins and fungal secondary metabolites were normalized as a proof of concept with the NAPS RI system under multiple pH, column and gradient chromatographic conditions. This method was then applied to the analysis of a crude extract of Penicillium roqueforti, where we were able to decrease the number of false positives by implementing an RI filter as well as a secondary correction factor. Additionally, we developed software that allows users to convert published RIs to a predicted tR values. Integration of the NAPS RI system into routine LC-MS analysis will improve compound identifications and help facilitate ease of data sharing.

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