An LC-MS/MS "dilute and shoot" method for the determination of 295 fungal and bacterial metabolites was optimized and validated according to the guidelines established in the Directorate General for Health and Consumer Affairs of the European Commission (SANCO) document No. 12495/2011. Four different types of food matrices were chosen for validation: apple puree for infants (high water content), hazelnuts (high fat content), maize (high starch and low fat content) and green pepper (difficult or unique matrix). Method accuracy and precision was evaluated using spiked samples in five replicates at two concentration levels. Method trueness was demonstrated through participation in various proficiency tests. Although the method covers a total number of 331 analytes, validation data were acquired only for 295 analytes, either due to the non-availability of analytical standards or due other reasons described in this paper. Concerning the apparent recovery, the percentage of 295 analytes matching the acceptable recovery range of 70-120% lied down by SANCO varied from 21% in green pepper to 74% in apple puree at the highest spiking level. At the levels close to limit of quantification only 20-58% of the analytes fulfilled this criterion. The extent of matrix effects was strongly dependent on the analyte/matrix combination. In general, the lowest matrix effects were observed in apple puree (59% of analytes were not influenced by enhancement/suppression at all at the highest validation level). The highest matrix effects were observed in green pepper, where only 10% of analytes did not suffer from signal suppression/enhancement. The repeatability of the method was acceptable (RSD≤20) for 97% of all analytes in apple puree and hazelnuts, for 95% in maize and for 89% in green pepper. Concerning the trueness of the method, Z-scores were generally between -2 and 2, despite a broad variety of different matrices. Based on these results it can be concluded that quantitative determination of mycotoxins by LC-MS/MS based on a "dilute and shoot" approach is also feasible in case of complex matrices.

Mycotoxin contamination of cereals and related products used for feed can cause intoxication, especially in farm animals. Therefore, efficient analytical tools for the qualitative and quantitative analysis of toxic fungal metabolites in feed are required. Current methods usually include an extraction step, a clean-up step to reduce or eliminate unwanted co-extracted matrix components and a separation step with suitably specific detection ability. Quantitative methods of analysis for most mycotoxins use immunoaffinity clean-up with high-performance liquid chromatography (HPLC) separation in combination with UV and/or fluorescence detection. Screening of samples contaminated with mycotoxins is frequently performed by thin layer chromatography (TLC), which yields qualitative or semi-quantitative results. Nowadays, enzyme-linked immunosorbent assays (ELISA) are often used for rapid screening. A number of promising methods, such as fluorescence polarization immunoassays, dipsticks, and even newer methods such as biosensors and non-invasive techniques based on infrared spectroscopy, have shown great potential for mycotoxin analysis. Currently, there is a strong trend towards the use of multi-mycotoxin methods for the simultaneous analysis of several of the important Fusarium mycotoxins, which is best achieved by LC–MS/MS (liquid chromatography with tandem mass spectrometry). This review focuses on recent developments in the determination of mycotoxins with a special emphasis on LC–MS/MS and emerging rapid methods.

The development of liquid chromatography-mass spectrometry (LC-MS)/mass spectrometry (MS) methods for the simultaneous detection and quantification of a broad spectrum of mycotoxins has facilitated the screening of a larger number of samples for contamination with a wide array of less well-known “emerging” mycotoxins and other metabolites. In this study, 83 samples of feed and feed raw materials were analysed. All of them were found to contain seven to 69 metabolites. The total number of detected metabolites amounts to 139. Fusarium mycotoxins were most common, but a number of Alternaria toxins also occurred very often. Furthermore, two so-called masked mycotoxins (i.e., mycotoxin conjugates), namely deoxynivalenol-3-glucoside (75% positives) and zearalenone-4-sulfate (49% positives), were frequently detected. Although the observed median concentrations of the individual analytes were generally in the low μg/kg range, evaluating the toxicological potential of a given sample is difficult. Toxicity data on less well-known mycotoxins and other detected metabolites are notoriously scarce, as an overview on the available information on the most commonly detected metabolites shows. Besides, the possible synergistic effects of co-occurring substances have to be considered.

This paper describes the validation of an LC-MS/MS-based method for the quantification of > 500 secondary microbial metabolites. Analytical performance parameters have been determined for seven food matrices using seven individual samples per matrix for spiking. Apparent recoveries ranged from 70 to 120% for 53–83% of all investigated analytes (depending on the matrix). This number increased to 84–94% if the recovery of extraction was considered. The comparison of the fraction of analytes for which the precision criterion of RSD ≤ 20% under repeatability conditions (for 7 replicates derived from different individual samples) and intermediate precision conditions (for 7 technical replicates from one sample), respectively, was met (85–97% vs. 93–94%) highlights the contribution of relative matrix effects to the method uncertainty. Statistical testing of apparent recoveries between pairs of matrices exhibited a significant difference for more than half of the analytes, while recoveries of the extraction showed a much better agreement. Apparent recoveries and matrix effects were found to be constant over 2–3 orders of magnitude of analyte concentrations in figs and maize, whereas the LOQs differed less than by a factor of 2 for 90% of the investigated compounds. Based on these findings, this paper discusses the applicability and practicability of current guidelines for multi-analyte method validation. Investigation of (apparent) recoveries near the LOQ seems to be insufficiently relevant to justify the enormous time-effort for manual inspection of the peaks of hundreds of analytes. Instead, more emphasis should be put on the investigation of relative matrix effects in the validation procedure.