Wageningen Academic World Mycotoxin Journal, 2021; 14 (1): 3-26 Publishers Developments in mycotoxin analysis: an update for 2019-2020 S.A. Tittlemier1*, J. Brunkhorst2, B. Cramer3, M.C. DeRosa4, V.M.T. Lattanzio5, R. Malone2, C. Maragos6, M. Stranska7 and M.W. Sumarah8 1Canadian Grain Commission, Grain Research Laboratory, 1404-303 Main St, Winnipeg, MB, R3C 3G8, Canada; 2Trilogy Analytical Laboratory, 870 Vossbrink Dr, Washington, MO 63090, USA; 3University of Münster, Institute of Food Chemistry, Corrensstr. 45, 48149 Münster, Germany; 4Department of Chemistry, Carleton University, Ottawa, ON, K1S 5B6, Canada; 5National Research Council of Italy, Institute of Sciences of Food Production, via Amendola 122/O, 70126 Bari, Italy; 6United States Department of Agriculture, ARS National Center for Agricultural Utilization Research, Peoria, IL 61604, USA; 7Department of Food Analysis and Nutrition, Faculty of Food and Biochemical Technology, University of Chemistry and Technology, Technicka 5, Prague, 166 28, Czech Republic; 8Agriculture and Agri-Food Canada, London Research and Development Centre, 1391 Sandford Street, London, ON, N5V 4T3, Canada; [email protected] Received: 18 November 2020 / Accepted: 16 December 2020 © 2021 Wageningen Academic Publishers OPEN ACCESS REVIEW ARTICLE Abstract This review summarises developments on the analysis of various matrices for mycotoxins published in the period from mid-2019 to mid-2020. Notable developments in all aspects of mycotoxin analysis, from sampling and quality assurance/ quality control of analytical results, to the various detection and quantitation technologies ranging from single mycotoxin biosensors to comprehensive instrumental methods are presented and discussed. Aside from sampling and quality control, discussion of this past year’s developments is organised by detection and quantitation technology and covers chromatography with targeted or non-targeted high resolution mass spectrometry, tandem mass spectrometry, detection other than mass spectrometry, biosensors, as well as assays that use alternatives to antibodies. This critical review aims to briefly present the most important recent developments and trends in mycotoxin determination as well as to address limitations of the presented methodologies. Keywords: sampling, multi-mycotoxin analysis, quality control, multiplex, biosensor, chromatography, mass spectrometry, aptamer, ELISA, molecularly imprinted polymer, high resolution mass spectrometry 1. Introduction and quantitation technologies available to the analyst. Our hope is that this reorganisation allows readers to more easily This article is the latest instalment in a series of annual access new information relevant to the analytical tools that reviews highlighting analytical method developments for they have at hand in order to improve their mycotoxin mycotoxin determination, continuing from the previous analyses. review covering the mid-2018 to mid-2019 period (Tittlemier et al., 2020a). As with the previous reviews Specific topics included in this review are sampling in this series, our primary purpose is to raise awareness (Section 2), quality control of mycotoxin analyses (Section of the developments and advances in analytical methods 3), chromatography with tandem mass spectrometry for mycotoxins published between mid-2019 to mid-2020. (MS/MS; Section 4), chromatography with targeted high resolution mass spectrometry (HRMS; Section 5), Globally, this past year has been full of changes. Our annual chromatography with non-targeted HRMS (Section 6), review has also changed. We have reorganised the review chromatography with non-mass spectrometric (MS) into (mostly) new sections that try to cover all aspects of detection (Section 7), multiplex biosensors (Section 8), mycotoxin analysis, from sampling and quality assurance/ single mycotoxin or single mycotoxin family biosensors quality control of analytical results, to the various detection (Section 9), and assays using antibody analogues (Section ISSN 1875-0710 print, ISSN 1875-0796 online, DOI 10.3920/WMJ2020.2664 3 S.A. Tittlemier et al. 10). A list of commonly used abbreviations in the article monitoring as a more intensive sampling plan was needed is provided in the Appendix. to minimise the probability of accepting a non-compliant batch to less than 5%. With this reorganisation we have still tried to maintain the annual review as a discussion of the most novel and Fischer et al. (2019) examined the main contributing relevant advances in analytical methodology as selected by factors to variance in the analysis of aflatoxins (AFs) in the experts in the broad field of ‘mycotoxin analysis’. As with truckloads of bagged maize delivering to Kenyan mills. past years, this review is not meant to be an exhaustive list of They also estimated the number of samples that would publications on mycotoxin analytical methods, nor a list of provide sufficient power to their sampling plan to assess incremental improvements of more ‘mature’ methodology. AFs concentration in a truckload of bagged maize. For the Critical comments on the selected methods, their validation experimental design, at each of three different commercial parameters, or unique applications are included to guide mills in Kenya three trucks were selected and 10 bags of readers in assessing the impact of these developments. This maize were each sampled twice from each of the three review should therefore appeal to all readers. trucks. Sampling of maize was performed manually, and the specific trier used to sample grain from bags was described. 2. Sampling Unfortunately, the procedures for bag sampling, bag selection, and sub-sampling of maize were not described Research on sampling and sample processing published in the paper. Concentrations of AFs varied greatly between over the past year covers a wide range of topics ranging bags sampled, and ranged from less than the detection from modelling to evaluate the costs of sampling plans limit to 1,100 μg/kg. The distribution of AFs in samples (Focker et al., 2019), grinding and dividing grain samples taken from truckloads of bagged maize differed from the in preparation for analysis (Tittlemier et al., 2020b), and the distribution observed in bulk maize transported by truck characterisation of mycotoxin (and variance of mycotoxin in Texas, emphasising that mycotoxin distributions in analysis) in fields (Cowger et al., 2020), stored grain (Kerry commodities depend on the handling and history of a et al., 2019), and transported grain (Fischer et al., 2019). commodity lot. Variability of AF concentrations in maize between bags (61% of total variance) and variability of AF Focker et al. (2019) expanded upon their previous work and concentrations in maize within a bag (27%) were the top two used optimisation modelling to assess sampling plans for factors contributing to the variance of AFs concentration monitoring aflatoxin B1 (AFB1) in maize. They evaluated observed in the study, followed by variability due to analysis six different scenarios of AFB1 production and monitoring (6%). The authors stated that the low variance from the along the multi-stage maize supply chain from the Black analysis step does not indicate ‘low risk from testing’ but Sea maize growing region to processors in the Netherlands. that the variance from the analysis step can be mitigated The six scenarios were set with AFB1 production and by good quality control procedures, such as routine use of monitoring at certain points of a simplified maize supply reference materials and control charting. The experimental chain. Production of AFB1 in the field was limited to occur data were also used to estimate that a minimum of 20 at three concentrations (1, 4, and 10 μg/kg) and production bags per truck need to be sampled in order to have a false during shipping (100 μg/kg in 5% of the maize transported). positive rate of 5% and a false negative rate of 20%. The Monitoring points were limited to storage bins used after authors outlined the assumptions used in their work, and harvest, ship compartments for sea transport, barges for noted that the concentrations of AFs were relatively low in domestic transport, and processing plants. The assumptions the year of their study but did not elaborate on how higher of the model were constraining and do not reflect the wide concentrations may affect the factors contributing to total variety of conditions encountered in real life, but they are variance nor affect the minimum number of bags to be thoroughly described by the authors. The model also sampled. Despite this limitation, the study provides good included the need to replace non-compliant batches of insight into how risk from AFs in maize can be managed at maize (i.e. AFB1 concentrations at the end of the supply a commercial mill, and demonstrates that modifications to chain greater than an industry specification of 2.5 μg/kg) the Kenyan sampling recommendation decreases the risk to meet grain volumes required by the processors. The of mischaracterising the AFs content of maize. optimisation model minimised the total cost of monitoring and replacing batches of non-compliant grain. As expected, Tittlemier et al. (2020b) evaluated grinders and dividing model outputs for the six scenarios showed that the optimal equipment used in the preparation of whole oats for number of maize batches to analyse, and the optimal mycotoxin analysis. The analysis of hulls from naturally monitoring point in the supply chain, depended upon the infected