Assessment of Human Mycotoxin Exposure in Hungary by Urinary Biomarker Determination and the Uncertainties of the Exposure Calculation: A Case Study.

Urinary biomarkers of mycotoxin exposure were evaluated in the case of healthy people (n = 41) and coeliac patients (n = 19) by using a multi-biomarker LC-MS/MS immunoaffinity based method capable to analyse biomarkers of nine mycotoxins, i.e., fumonisin B1 (FB1), fumonisin B2 (FB2), deoxynivalenol (DON), zearalenone (ZEN), ochratoxin A (OTA), Aflatoxin B1 (AFB1), T-2 toxin, HT-2 toxin and Nivalenol (NIV). Urinary biomarker concentrations were used to calculate the probable daily intake (PDI) of fumonisin B1, deoxynivalenol, zearalenone and ochratoxin A and compared with their tolerable daily intake (TDI). The human urinary excretion rate values reported in the literature and the 24 h excretion rate measured in piglets were used to estimate and compare the PDI values of the four mycotoxins. The highest mean biomarker concentrations were found for DON (2.30 ng/mL for healthy people and 2.68 ng/mL for coeliac patients). Mean OTA concentration was significantly higher (p < 0.001) in healthy people compared to coeliac patients. PDI calculated with piglets excretion data exceeded the TDI values by a much smaller percentage than when they were calculated from human data, especially for FB1. The uncertainties arising from the different calculations can be well perceived on the basis of these data.

Occurrence and Risk Assessment of Fumonisin B1 and B2 Mycotoxins in Maize-Based Food Products in Hungary.

Fumonisins are toxic secondary metabolites produced mainly by Fusarium verticillioides and Fusarium proliferatum. Their toxicity was evaluated, and health-based guidance values established on the basis of both Joint FAO/WHO Expert Committee on Food Additives (JECFA) and European Food Safety Authority (EFSA) recommendations. This study presents the results of fumonisin analyses in different maize- and rice-based food products in Hungary and the potential health risk arising from their dietary intake. In total, 326 samples were measured in 2017 and 2018 to determine fumonisins B1 and B2 levels. Three-day dietary record data were collected from 4992 consumers, in 2009. For each food category, the average concentration values were multiplied by the relevant individual consumption data, and the results were compared to the reference values. With respect to the maximum limits, one maize flour, two maize grits, and two samples of other maize-based, snack-like products had total fumonisin content minimally exceeding the EU regulatory limit. The mean daily intake for all maize-product consumers was 0.045-0.120 µg/kg bw/day. The high intake (95 percentile) ranged between 0.182 and 0.396 µg/kg bw/day, well below the 1 µg/kg bw/day tolerable daily intake (TDI) established by EFSA. While the intake calculations resulted in comforting results, maize-based products may indeed be contaminated by fumonisins. Therefore, frequent monitoring of fumonisins' levels and evaluation of their intakes using the best available data are recommended.

Analysis of industry-generated data. Part 2: Risk-based sampling plan for efficient self-control of aflatoxin M1 contamination in raw milk.

Aflatoxin M1 (AFM1) contamination in 21,969 milk samples taken in Italy during 2005-08 and 2010 provided the basis for designing an early warning self-control plan. Additionally, 4148 AFM1 data points from the mycotoxin crisis (2003-04) represented the worst case. No parametric function provided a good fit for the skewed and scattered AFM1 concentrations. The acceptable reference values, reflecting the combined uncertainty of AFM1 measured in consignments consisting of milk from one to six farms, ranged from 40 to 16.7 ng kg(-1), respectively. Asymmetric control charts with these reference values, 40 and 50 ng kg(-1) warning and action limits are recommended to assess immediately the distribution of AFM1 concentration in incoming consignments. The moving window method, presented as a worked example including 5 days with five samples/day, enabled verification of compliance of production with the legal limit in 98% of the consignments at a 94% probability level. The sampling plan developed assumes consecutive analyses of samples taken from individual farms, which makes early detection of contamination possible and also immediate corrective actions if the AFM1 concentration in a consignment exceeds the reference value. In the latter case different control plans with increased sampling frequency should be applied depending on the level and frequency of contamination. As aflatoxin B1 increases in feed at about the same time, therefore a coordinated sampling programme performed by the milk processing plants operating in a confined geographic area is more effective and economical then the individual ones. The applicability of the sample size calculation based on binomial theorem and the fast response rate resulting from the recommended sampling plan were verified by taking 1000-10,000 random samples with replacement from the experimental databases representing the normal, moderately and highly contaminated periods. The efficiency of the control plan could be substantially enhanced if the dairy farms used feed with a tolerable level of AFB1.

Nature of the field-to-field distribution of pesticide residues.

The supervised trial datasets (1950), consisting of a minimum of five residue values and selected by the experts of FAO/WHO Joint Meeting on Pesticide Residues for recommending maximum residue levels between 1997 and 2011, were evaluated to obtain information on the typical spread of residue values in individual datasets. The typical relative standard deviation, CV, of field-to-field variation of pesticide residues was about 80%. The spread of residues in datasets is independent from the chemical structure of pesticides, residue level, pre-harvest interval and number of values in the datasets. The CV ranges within the Codex commodity groups and between groups overlapped and their difference were not statistically significant. The number of residues below the limit of quantification (LOQ) affects the CV at various extents depending on the ratio of LOQ/R mean. The combined uncertainty of the highest residue in a dataset significantly affects the CV of the dataset. The lowest and intermediate ones have less influence. The residues in different fields receiving the same treatment vary within large range: 55%, 72%, 78%, 86% and 89% of the 25,766 residues values were, respectively, within 3, 4, 5, 6 and 7 times the median value of the corresponding dataset.