Carry-Over of Aflatoxin B1 from Feed to Cow Milk-A Review.

The conversion of aflatoxin B1 in feed consumed by cows into aflatoxin M1 in their milk poses a challenge to food safety, with milk being a popular staple food and because of the harmful effects of these compounds. This study aimed at reviewing scientific information about the extent of carry-over of AFB1 from feed to milk. A range of studies reported about correlations of carry-over with different factors, particularly with milk yield and AFB1 intake. The extent of carry-over considerably varies, being 1-2% on average, which may be as high as 6% in the case of increased milk production. Specific factors influencing transfer rates, including milk yield, somatic cell counts, aflatoxin B1 intake, source of contamination, seasonal effects, particle size of feed, and the effects of certain interventions, namely vaccination and the use of adsorbents, are identified as the most important and are discussed in this review. The different mathematical formulas describing carry-over and instances of their application are reviewed as well. These carry-over equations may lead to largely different results, and no single carry-over equation can be suggested as the best one. While the exact quantification of carry-over is difficult as the process depends on many factors, including individual variabilities between animals, the intake of aflatoxin B1 and milk yield seem to be the most important factors influencing the excreted amount of aflatoxin M1 and the rate of carry-over.

Emerging risk identification by applying data analytical tools.

The working programme 'Emerging risk identification by applying data analytical tools' was delivered by the Digital Food Chain Education, Research, Development, and Innovation Institute (Digital Food Institute, DFI) on the field of emerging risks at the University of Veterinary Medicine Budapest, Hungary. The Institute is the University's research and education unit that provides data analysis and research along the whole food chain and takes networking in this area to a new level. The Fellow joined the hub of experts and researchers in the field of food chain safety data analysis, responsible for protecting public health concerning food in Hungary. The programme consisted of several different activities to provide an overview of the different tools that can be employed in the emerging risk identification process and prepare various stakeholders for new food chain safety issues. The programme was split into four modules to run over the one-year fellowship covering different areas of data analysis and emerging risk identification. The aim was to be fully integrated with the organisation's work experience, increase knowledge of scientific aspects relevant in the field of data analysis and visualisation tools in the emerging risk identification area, and implement the results into various EU stakeholders' environments assessments.

Probabilistic modeling and risk characterization of the chronic aflatoxin M1 exposure of Hungarian consumers.

Aflatoxin contamination can appear in various points of the food chain. If animals are fed with contaminated feed, AFB1 is transformed-among others-to aflatoxin M1 (AFM1) metabolite. AFM1 is less toxic than AFB1, but it is still genotoxic and carcinogenic and it is present in raw and processed milk and all kinds of milk products. In this article, the chronic exposure estimation and risk characterization of Hungarian consumers are presented, based on the AFM1 contamination of milk and dairy products, and calculated with a probabilistic method, the two-dimensional Monte-Carlo model. The calculations were performed using the R plugin (mc2d package) integrated into the KNIME (Konstanz Information Miner) software. The simulations were performed using data from the 2018-2020 food consumption survey. The AFM1 analytical data were derived from the Hungarian monitoring survey and 1,985 milk samples were analyzed within the framework of the joint project of the University of Debrecen and the National Food Chain Safety Office of Hungary (NÉBIH). Limited AFM1 concentrations were available for processed dairy products; therefore, a database of AFM1 processing factors for sour milk products and various cheeses was produced based on the latest literature data, and consumer exposure was calculated with the milk equivalent of the consumed quantities of these products. For risk characterization, the calculation of hazard index (HI), Margin of Exposure, and the hepatocellular carcinoma incidence were used. The results indicate that the group of toddlers that consume a large amount of milk and milk products are exposed to a certain level of health risk. The mean estimated daily intake of toddlers is in the range of 0.008-0.221 ng kg-1 bw day-1; the 97.5th percentile exposure of toddlers is between 0.013 ng kg-1 bw day-1 and 0.379 ng kg-1 bw day-1, resulting in a HI above 1. According to our study, the exposure of older age groups does not pose an emergent health risk. Nevertheless, the presence of carcinogenic compounds should be kept to a minimum in the whole population.

A Systematic Review of the Efficacy of Interventions to Control Aflatoxins in the Dairy Production Chain-Feed Production and Animal Feeding Interventions.

The study presents a systematic review of published scientific articles investigating the effects of interventions aiming at aflatoxin reduction at the feed production and animal feeding phases of the milk value chain in order to identify the recent scientific trends and summarize the main findings available in the literature. The review strategy was designed based on the guidance of the systematic review and knowledge synthesis methodology that is applicable in the field of food safety. The Web of Science and EBSCOhost online databases were searched with predefined algorithms. After title and abstract relevance screening and relevance confirmation with full-text screening, 67 studies remained for data extraction, which were included in the review. The most important identified groups of interventions based on their mode of action and place in the technological process are as follows: low-moisture production using preservatives, acidity regulators, adsorbents and various microbiological additives. The results of the listed publications are summarized and compared for all the identified intervention groups. The paper aimed to help feed producers, farmers and relevant stakeholders to get an overview of the most suitable aflatoxin mitigation options, which is extremely important in the near future as climate change will likely be accompanied by elevated mycotoxin levels.

Corrigendum: Detection of Aflatoxins in Different Matrices and Food-Chain Positions.

[This corrects the article DOI: 10.3389/fmicb.2020.01916.].

Pre-Harvest Modelling and Mitigation of Aflatoxins in Maize in a Changing Climatic Environment-A Review.

Aflatoxins (AFs) are harmful secondary metabolites produced by various moulds, among which Aspergillus flavus is the major AF-producer fungus. These mycotoxins have carcinogenic or acute toxigenic effects on both humans and food producing animals and, therefore, the health risks and also the potential economic damages mounted by them have led to legal restrictions, and several countries have set maximum allowable limits for AF contaminations in food and feed. While colonization of food and feed and AF production by A. flavus are highly supported by the climatic conditions in tropical and subtropical geographic regions, countries in the temperate climate zones are also increasingly exposed to AF-derived health risks due to climate change. In the present study, we have reviewed the available mathematical models as risk assessment tools to predict the possibility of A. flavus infection and levels of AF contaminations in maize in a changing climatic environment. After highlighting the benefits and possible future improvements of these models, we summarize the current agricultural practices used to prevent or, at least, mitigate the deleterious consequences of AF contaminations.

Detection of Aflatoxins in Different Matrices and Food-Chain Positions.

Aflatoxins, produced mainly by filamentous fungi Aspergillus flavus and Aspergillus parasiticus, are one of the most carcinogenic compounds that have adverse health effects on both humans and animals consuming contaminated food and feed, respectively. Aflatoxin B1 (AFB1) and aflatoxin B2 (AFB2) as well as aflatoxin G1(AFG1) and aflatoxin G2 (AFG2) occur in the contaminated foods and feed. In the case of dairy ruminants, after the consumption of feed contaminated with aflatoxins, aflatoxin metabolites [aflatoxin M1 (AFM1) and aflatoxin M2 (AFM2)] may appear in milk. Because of the health risk and the official maximum limits of aflatoxins, there is a need for application of fast and accurate testing methods. At present, there are several analytical methods applied in practice for determination of aflatoxins. The aim of this review is to provide a guide that summarizes worldwide aflatoxin regulations and analytical methods for determination of aflatoxins in different food and feed matrices, that helps in the decision to choose the most appropriate method that meets the practical requirements of fast and sensitive control of their contamination. Analytical options are outlined from the simplest and fastest methods with the smallest instrument requirements, through separation methods, to the latest hyphenated techniques.

Occurrence of Aflatoxin M1 in Raw Milk Marketed in Italy: Exposure Assessment and Risk Characterization.

The current study is based on the AFM1 contamination of milk determined from April 2013 to December 2018 in the framework of a self-control plan of six milk processing plants in Italy. These data - together with the consumption data of milk consumers - were evaluated and used for the calculation of the Estimated Daily Intake (EDI), the Hazard Index (HI), and the fraction of hepatocarcinoma cases (HCC) due to AFM1 exposure in different population groups. Altogether a total of 31,702 milk samples were analyzed, representing 556,413 tons of milk, which is an outstanding amount compared to published studies. The results indicate the monthly fluctuation of AFM1 levels through a period of nearly 6 years. The EDI of AFM1 in different population groups was in the range of 0.025-0.328 ng kg-1 body weight (bw) per day, based on the average consumption levels and weighted mean contamination of the milk in the study period. Considering average consumptions, in the groups of infants and toddlers, the HI calculation resulted in 1.64 and 1.4, respectively, while for older age groups, it was <1. The estimated fractions of HCC incidences attributable to the AFM1 intakes were 0.005 and 0.004 cases per 100,000 individuals in the 0-0.9 and 1-2.9-year age groups, respectively, and below 0.004 cases in the other age categories. The monthly average AFM1 contamination of tested milk consignments ranged between 7.19 and 22.53 ng kg-1. Although the results of this extensive investigation showed a low risk of HCC, the variability of climatic conditions throughout years that influence AFB1 contamination of feed and consequently AFM1 contamination of milk justifies their continuous monitoring and update of the risk assessment.

An effective self-control strategy for the reduction of aflatoxin M1 content in milk and to decrease the exposure of consumers.

The study reports the results of testing the sensitivity of an early warning sampling plan for detecting milk batches with high aflatoxin AFM1 concentration. The effectiveness of the method was investigated by the analysis of 9017 milk samples collected in Italian milk processing plants that applied control plans with different action limits (AL). For those milk processing plants where 30 ng kg-1 AL has been applied, the AFM1 contamination was significantly lower at or above the 95th percentile of the milk samples when compared with plants that used 40 ng kg-1 AL. The results show that the control plan can be used effectively for early warning of occurrence of high AFM1 contamination of milk and to carry out pro-active measures to limit the level of contamination. Estimation of dietary exposure was also carried out, based on the aflatoxin M1 content of the milk samples and on Italian food consumption data. Estimated Daily Intakes (EDI) and Hazard Indices (HI) were calculated for different age groups of the population. HIs show that no adverse effects are expected for the adult population, but in the case of children under age three, the approximate HI values were considerably higher. This underlines the importance of the careful monitoring and control of aflatoxin M1 in milk and dairy products.

Statistical and population genetics issues of two Hungarian datasets from the aspect of DNA evidence interpretation.

When the DNA profile from a crime-scene matches that of a suspect, the weight of DNA evidence depends on the unbiased estimation of the match probability of the profiles. For this reason, it is required to establish and expand the databases that reflect the actual allele frequencies in the population applied. 21,473 complete DNA profiles from Databank samples were used to establish the allele frequency database to represent the population of Hungarian suspects. We used fifteen STR loci (PowerPlex ESI16) including five, new ESS loci. The aim was to calculate the statistical, forensic efficiency parameters for the Databank samples and compare the newly detected data to the earlier report. The population substructure caused by relatedness may influence the frequency of profiles estimated. As our Databank profiles were considered non-random samples, possible relationships between the suspects can be assumed. Therefore, population inbreeding effect was estimated using the FIS calculation. The overall inbreeding parameter was found to be 0.0106. Furthermore, we tested the impact of the two allele frequency datasets on 101 randomly chosen STR profiles, including full and partial profiles. The 95% confidence interval estimates for the profile frequencies (pM) resulted in a tighter range when we used the new dataset compared to the previously published ones. We found that the FIS had less effect on frequency values in the 21,473 samples than the application of minimum allele frequency. No genetic substructure was detected by STRUCTURE analysis. Due to the low level of inbreeding effect and the high number of samples, the new dataset provides unbiased and precise estimates of LR for statistical interpretation of forensic casework and allows us to use lower allele frequencies.

Use of Combined Uncertainty of Pesticide Residue Results for Testing Compliance with Maximum Residue Limits (MRLs).

The uncertainty of pesticide residue levels in crops due to sampling, estimated for 106 individual crops and 24 crop groups from residue data obtained from supervised trials, was adjusted with a factor of 1.3 to accommodate the larger variability of residues under normal field conditions. Further adjustment may be necessary in the case of mixed lots. The combined uncertainty of residue data including the contribution of sampling is used for calculation of an action limit, which should not be exceeded when compliance with maximum residue limits is certified as part of premarketing self-control programs. On the contrary, for testing compliance of marketed commodities the residues measured in composite samples should be greater than or equal to the decision limit calculated only from the combined uncertainty of the laboratory phase of the residue determination. The options of minimizing the combined uncertainty of measured residues are discussed. The principles described are also applicable to other chemical contaminants.

Estimation of sampling uncertainty of pesticide residues based on supervised residue trial data.

Typical sampling uncertainties were calculated as the average of relative standard deviations (CV) of residues measured in individual crops tested in supervised residue trials and from their pooled variance for crop groups. The relative confidence intervals of the sampling uncertainty for different crops were estimated from the random duplicate composite samples generated with computer modeling from residues in 182 independent primary sample sets, each consisting of 100-320 residue data. The relative 95% confidence intervals were found to be independent from the CV of primary residue data populations; therefore, the calculated values are generally applicable. In view of the potentially serious consequences of underestimated sampling uncertainties, their upper confidence limits are recommended for practical use to verify the compliance of products and for planning statistically based sampling programs. Sampling uncertainties are reported for 24 crop groups and 106 individual crops.

Analysis of industry-generated data. Part 1: a baseline for the development of a tool to assist the milk industry in designing sampling plans for controlling aflatoxin M1 in milk.

The presence of aflatoxin M1 (AFM1) in milk was assessed in Italy in the framework of designing a monitoring plan actuated by the milk industry in the period 2005-10. Overall, 21,969 samples were taken from tankers collecting milk from 690 dairy farms. The milk samples were representative of the consignments of co-mingled milk received from multiple (two to six) farms. Systematic, biweekly sampling of consignments involved each of the 121 districts (70 in the North, 17 in the Central and 34 in the South regions of Italy). AFM1 concentration was measured using an enzyme-linked immunoassay method (validated within the range of 5-100 ng kg(-1)) whereas an HPLC method was used for the quantification of levels in the samples that had concentrations higher than 100 ng kg(-1). Process control charts using data collected in three processing plants illustrate, as an example, the seasonal variation of the contamination. The mean concentration of AFM1 was in the range between 11 and 19 ng kg(-1). The 90th and 99th percentile values were 19-34 and 41-91 ng kg(-1), respectively, and values as high as 280 ng kg(-1) were reached in 2008. The number of non-compliant consignments (those with an AFM1 concentration above the statutory limit of 50 ng kg(-1)) varied between 0.3% and 3.1% per year, with peaks in September, after the maize harvest season. The variability between different regions was not significant. The results show that controlling the aflatoxins in feed at farm level was inadequate, consequently screening of raw milk prior to processing was needed. The evaluation of the AFM1 contamination level observed during a long-term period can provide useful data for defining the frequency of sampling.

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.

Limitations in the determination of maximum residue limits and highest residues of pesticides: Part I.

The pesticide usages are controlled by comparing residue concentrations in treated commodities to legally permitted maximum levels (MRLs) determined based on supervised trials designed to reflect likely maximum residues occurring in practice following authorised use. The number of trials available may significantly affect the accuracy of estimated maximum residues. We conducted a study with synthetic lognormal distributions with mean of 1 and standard deviations of 0.8 and 1.0, which reflect the residue distributions observed in practice. The likely residues in samples were modelled by drawing random samples of size 3, 5, 10 and 25 from the synthetic populations. The results indicate that the estimations of highest residues (HR), used for calculation of short-term intake, and the MRLs, serving as legal limits, are very uncertain based on 3-5 trials indicated by the calculated HR0.975/HR0.025 and MRL0.975/MRL0.025 ratios of 12 and 9, and 13 and 10, respectively, which question the suitability of such trials for the intended purpose. As the 95% range of HR and MRL rapidly decreases with number of trials, ideally ≥15 but minimum 6-8 trials should be used for estimation of HR and MRL according to the current typical practice of Codex Alimentarius.

Estimation of sampling uncertainty for pesticide residues in root vegetable crops.

The sampling uncertainty for pesticide residues in carrots, parsley leaves and selected medium size crops was estimated with simple random sampling by applying range statistics. The primary samples taken from treated fields consisted of individual carrots or a handful of parsley leaves. The samples were analysed with QUEChERs extraction method and LCMS/MS detection with practical LOQ of 0.001 mg/kg. The results indicate that the average sampling uncertainties estimated with simple random sampling and range statistics were practically the same. The confidence interval for the estimated sampling uncertainty decreased with the number of replicate samples taken from one lot and the number of lots sampled. The estimated relative ranges of sampling uncertainty are independent from the relative standard deviation of the primary samples. Consequently the conclusions drawn from these experiments are generally applicable. There is no optimum for sample size and number of lots to be tested for estimation of sampling uncertainty. Taking a minimum of 6 replicate samples from at least 8-12 lots is recommended to obtain a relative 95% range of sampling uncertainty within 50%. The cost of sampling/analyses, the consequences of wrong decision should also be taken into account when a sampling plan is prepared.