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Exposure assessment of mycotoxins in dairy milk

Rory Coffey *, Enda Cummins, Shane WardSchool of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland

a r t i c l e i n f o

Article history:Received 29 January 2008Received in revised form 22 April 2008Accepted 13 May 2008

Keywords:MycotoxinsMilkExposure assessment

a b s t r a c t

The objective of this study was to develop a quantitative Monte Carlo exposure assessment model formycotoxins in dairy milk and to assess the potential human exposure levels. Mean concentrations ofmycotoxins in milk were estimated using the simulation model (Aflatoxin M1 = 0.0161 lg/kg, OchratoxinA = 0.0002 lg/kg, Deoxynivalenol = 1 lg/kg, Fumonisin B1 = 0.36 lg/kg, Zearalenone = 0.39 lg/kg, T-2 =0.0722 lg/kg) while the simulated tolerable daily intakes (TDIs) from milk for males and females all fellbelow European Union guidelines. Aflatoxin M1 was the toxin of greatest concern as it had potential toexceed the EU limit of 0.05 lg/kg in milk. The sensitivity analysis identified the concentration of toxins inmaize as the area which needs most attention in relation to crop management and agricultural practice.The sensitivity analysis assessed also identified the carry over rate as a factor closely related to risk and asa factor which required further research.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Quantitative Exposure Assessment is a methodology used toanalyse scientific information in order to estimate the probabilityand severity of an adverse event. This methodology was appliedto model the human exposure to mycotoxins resulting from myco-toxin contamination of dairy feed, subsequently carried over todairy milk for human consumption. Mycotoxins are secondarymetabolites of fungi and are produced when cereals or animal feedare colonised by moulds. Excretion of such toxins in bovine milkhas been documented (Blüthgen, Hammer, & Teufel, 2004;Yiannikouris & Jouany, 2002) and their carryover to dairy producerepresents a potential threat to human health. Studies have dem-onstrated that human dietary exposure to mycotoxins may leadto severe illness and can lead to liver cancer (Marquardt, 1996;Notermans, 2003). The mycotoxins which are focussed on in thisstudy and their effects on human health are displayed in Table 1.This assessment specifically focused on six mycotoxins of concernto humans (Aflatoxin B1/M1, Ochratoxin A, Deoxynivalenol,Fumonisin B1, Zearalenone and T-2 toxin) and involved analysingdata on the occurrence of these mycotoxins in three dairy feedingredients (barley, wheat and maize), inclusion rates in dairy feed,carryover rates to milk and subsequent human exposure. By com-bining the estimated individual mycotoxin concentrations in milkwith available consumption data for the Irish population, the dailyintake of mycotoxins from milk by individuals was calculated. Theexposure was characterised by the probability that viable myco-toxin concentrations were in milk at the time of consumption.

Information and data for the development of the model were ob-tained from Irish studies and expert opinion and, when not avail-able, from research in other countries. The basic model structureis given in Fig. 1.

2. Materials and methods

The developed model relies upon the generation of randomvariables from input probability distributions and these are repre-sented in the model equations by the name of the probability dis-tribution (e.g. Poisson, triangular etc.) followed by the parametersin brackets. The model used Monte Carlo simulation techniques(Vose, 2000) to create the output distributions. Monte Carlo meth-ods repeatedly select values randomly from distributions to createmultiple scenarios of a problem. Together, these scenarios give arange of possible solutions, some of which are more probableand some less probable, resulting in a probability distribution forthe solution parameter.

2.1. Model inputs

Individual levels of mycotoxins simulated in the model are dis-cussed. A summary of data and model inputs is given in Table 2.

2.1.1. Mycotoxin contamination in feed ingredients2.1.1.1. Aflatoxin B1. From the literature reviewed, it was consid-ered that there was only a remote chance of Aflatoxin B1 (AFB1)contamination in feed ingredients (barley, wheat) produced withinIreland (D’Mello, Placinta, & Macdonald, 1999; Larsen, Hunt, Perrin,& Ruckenbauer, 2004; Placinta, D’Mello, & Macdonald, 1999).This was due to the fact that the Irish climate does not favour

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* Corresponding author. Tel.: +353 1 7162164.E-mail address: rory.coffey@ucd.ie (R. Coffey).

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the formation of Aflatoxins in cereals. However, a lot of the compo-nents of bovine concentrate feed are imported. For example, maizefrom other countries was identified as a possible source of Afla-toxin B1 contamination of dairy cow concentrate feeds. To assessthe presence of Aflatoxin B1 in maize, data from a survey by TheMinistry for Agriculture, Food and Fisheries (MAFF, 1999), whichanalysed maize imported into the UK intended for use in animalfeed, was used. 139 samples in total were analysed with the limitof detection (LOD) being 0.1 lg/kg. Any samples below the limit ofdetection (i.e. value was between 0 and 0.1 lg/kg) were assumedto be equal to 0 lg/kg (see [i] in Section 2.1.6). Out of the 139 sam-ples, 51 proved positive for Aflatoxin B1 with the highest concen-tration being 16.4 lg/kg. The uncertainty about the probability ofcontamination (PAmz) was modelled using a beta distribution(n = 139, s = 51) to assess the probability of Aflatoxin B1 beingpresent in maize. A beta distribution can be used to model the con-fidence one has about the probability of success of a binomial trialp, where one has observed n independent trials of which s weresuccesses with the formula p = Beta (s + 1, n � s + 1) (Vose, 2000).A beta distribution with uniform prior is therefore used to modeluncertainty about contamination probability estimates in thisstudy. A Boolean flag was used to determine if a sample was con-taminated, with 0 indicating no contamination and 1 indicatingcontamination. Level ranges (LRPAmz) of 0–0.1 lg/kg (range 1: 88samples), 0.1–1 lg/kg (range 2: 36 samples), 1.1–2 lg/kg (range3: 5 samples) and >2 lg/kg (range 4: 10 samples) were used forthe detection of Aflatoxin B1 in maize in the survey. The probabil-ity within each range was also modelled using discrete distribu-tions. To model the level of Aflatoxin B1 in maize the sequenceof events is as follows:

1. A Boolean flag indicates if mycotoxins are present or not.2. Should mycotoxins be present (boolean flag = 1), it is assigned

one of the 4 level ranges using a discrete probability distribu-tion based on data by MAFF.

3. An individual level is then assigned by using a uniform distribu-tion with the minimum equal to the minimum value of therange and the maximum equal to the maximum within therange. The sequence of events can be seen in Fig. 2.

For example to illustrate this, from 139 samples 51 were positivefor Aflatoxin B1. Hence a Boolean flag will indicate the presenceor absence e.g. Binomial[1, beta(51 + 1, 139 � 51 + 1)] mean =0.38981. Should this be a positive result (i.e. = 1) then a level rangeis assigned based on the following probabilities: probability inrange 1 = beta (88 + 1, 139 � 88 + 1); probability in range 2 = beta(36 + 1, 139 � 36 + 1); probability in range 3 = beta (5 + 1, 139 �5 + 1); probability in range 4 = beta (10 + 1, 139 � 10 + 1). Theseprobabilities are normalised in the model so they sum to 1 (Vose,2000). When a range is assigned the actual level is modelled usinga uniform distribution giving concentrations for Aflatoxin B1 inmaize (LAmz). A negative binomial distribution has previously beenused to represent the level of mycotoxins in foods (Knutti & Schlat-ter, 1982; Whitaker, 1977). However, the model developed in thisstudy does not assume any distribution for the level of mycotoxins

but rather the model relies upon data from scientific sources (e.g.MAFF, 1999 [for Aflatoxin in maize]) to represent the level of myco-toxins. Thus, any distribution will be inherent in the form of theprobability of each contaminant range level. Hence, the resultingdistribution for the level of mycotoxins will follow the distributionof the data and hence inherently be distributed accordingly,whether it is negatively binomial or otherwise distributed. Dataand model inputs are summarised in Table 2.

2.1.1.2. Ochratoxin A. To estimate the typical concentrations ofOchratoxin A (OTA) in barley, a survey of stored grain by Prickett,Macdonald, and Wildley (1999) was examined. Out of 106 barleysamples analysed, 20 proved positive for Ochratoxin A with a min-imum concentration of 0.3 lg/kg and a maximum concentration of117 lg/kg. The probability of Ochratoxin A being present in barley(PBb) was modelled using a beta distribution (n = 106, s = 20). Toaccount for the uncertainty in the level of Ochratoxin A in barley(LBb) a cumulative distribution based on Prickett et al. data wasused (Fig. 3). The same study also surveyed wheat for OchratoxinA. In all 201 samples were examined. 32 were positive for Ochra-toxin A with concentrations ranging from 0.3 to 231 lg/kg. Theprobability of Ochratoxin A occurring in wheat (PBw) was againmodelled using a beta distribution (n = 201, s = 32) and the vari-ability of Ochratoxin A levels in wheat (LBw) was represented bya cumulative distribution based on the concentration of individualsamples recorded.

To model the level of Ochratoxin A in maize the survey by MAFF(1999) was used. Out of the 139 samples, only 14 were found tocontain traces of Ochratoxin A. This uncertainty was modelledusing a beta distribution to assess the probability of OchratoxinA being present in maize (PBmz). A boolean flag was used todetermine if a sample was contaminated, with 0 indicating nocontamination and 1 indicating contamination. The maximumconcentration found was 1.5 lg/kg. The level ranges (LRPBmz) with-in the survey were 0–0.1 lg/kg (125 samples), 0.1–1 lg/kg (12samples), 1.1–1.4 lg/kg (2 samples) and >1.5 lg/kg (0 samples).The same procedure as used for Aflatoxin was used to assign levelsof Ochratoxin A to maize samples (LBmz) (inputs and data are givenin Table 2).

2.1.1.3. Deoxynivalenol. The survey carried out by Prickett et al.(1999) in the UK also tested stored barley and wheat for Deoxyni-valenol. In relation to barley there were 75 positive samples out ofthe total of 106. The probability of Deoxynivalenol being present inbarley (PCb) was modelled using a beta distribution (n = 106,s = 75). The minimum recorded positive concentration was 20 lg/kg and the maximum was 370 lg/kg. Any samples below the limitof detection were assumed to be 0 lg/kg. To account for this vari-ability, the level of Deoxynivalenol in barley (LCb) was modelledusing a cumulative distribution based on the data collected byPrickett et al. (1999) and is displayed in Fig. 4. There were 198positive samples out of 201 for wheat. Again a beta distributionrepresented the probability of Deoxynivalenol occurring in wheat(PCw) (n = 201, s = 198). The positive values ranged between24 lg/kg and 600 lg/kg (Table 3). A cumulative distribution

Table 1Mycotoxins and their effects on human health

Mycotoxin Possible risk to human Reference

Aflatoxin B1/M1 Liver cancer IARC (1987), Creppy (2002)Ochratoxin A Possible carcinogen, kidney damage Petzinger and Weidenbach (2002), Creppy (2002)Deoxynivalenol Nausea, diarrhoea, vomiting and headache Tritscher and Page (2004), Meky et al. (2003)Fumonisin B1 Possible carcinogen, kidney/liver damage Turner et al. (1999), Quillien (2002)Zearalenone Natural oestrogen (effects undefined) Kuiper-Goodman et al. (1987), Quillien (2002)T-2 toxin Nausea, diarrhoea, vomiting and headache Tritscher and Page (2004), Meky et al. (2003)

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harvest

Bovine exposure

• Feed inclusion rate

Carry over rate to milk

Concentration in milk

Human consumption

• Consumption data

Pro

babi

lity

dens

ity

Concentration (µg/kg)

Carry over percentage

Pro

babi

lity

dens

ity

Concentration in milk (µg/kg)Pro

babi

lity

dens

ity

Concentration in cereals at

Pro

babi

lity

dens

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Exposurefrom milk (Logµg/kg bw/day)

Risk management 0.005

0.065

0.103

0.133

0.214

0.264

0.881

-0.5 -0.25 0 0.25 0.5 0.75 1

Rank Correlation

Sensitivity analysis

Exposure assessment

Policy change

Fig. 1. Model structure for simulating feed to food transfer of mycotoxins in bovine milk.

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Table 2Model distributions and inputs for mycotoxin contamination level, carry over rate and concentration in milk

Symbol Description Distribution Units

MycotoxinA = Aflatoxin M1/B1 (AFM1)

PAmz Probability of AFB1 presence or absence in maize Beta (n = 139, s = 51) FractionLRPAmz Level range probability for maize (lg/kg) Discrete (L1, L2, L3, L4; P1, P2, P3, P4)

P1 probabiliy of level range L1 (0–0.1) Beta (n = 139, s = 88) FractionP2 probabiliy of level range L2 (0.1–1) Beta (n = 139, s = 36) FractionP3 probabiliy of level range L3 (1.1–2) Beta (n = 139, s = 5) FractionP4 probabiliy of level range L4 (>2) Beta (n = 139, s = 10) Fraction

LAmz Level of AFB1 contamination in maize Uniform (min = range min, max = range max � from LRP)AVA Average AFB1 concentration in feed Imz�LAmz ug/kgCOA AFM1 carry over rate to milk Exponential %CMA Concentration of AFM1 in milk AVA � COA ug/kg

B = Ochratoxin A (OTA)PBb Probability of OTA presence or absence in barley Beta (n = 106, s = 20) FractionPBw Probability of OTA presence or absence in wheat Beta (n = 201, s = 32) FractionPBmz Probability of OTA presence or absence in maize Beta (n = 139, s = 14) FractionLBb Level of OTA contamination in barley Cumulative (see text) ug/kgLBw Level of OTA contamination in wheat Cumulative (see text) ug/kgLRPBmz Level range probability for maize (lg/kg) Discrete (L1, L2, L3, L4; P1, P2, P3, P4)

P1 probabiliy of level range L1 (0–0.1) Beta (n = 139, s = 125) FractionP2 probabiliy of level range L2 (0.1–1) Beta (n = 139, s = 12) FractionP3 probabiliy of level range L3 (1.1–1.4) Beta (n = 139, s = 2) FractionP4 probabiliy of level range L4 (>1.5) Beta (n = 139, s = 0) Fraction

LBmz Level of OTA contamination in maize Uniform (min = range min, max = range max � from LRP) ug/kgAVB Average OTA concentration in feed (Ib � LBb) + (Iw � LBw) + (Imz � LBmz) ug/kgCOB OTA carry over rate to milk Fixed value percentageCMB Concentration of OTA in milk AVB � COB ug/kg

C = Deoxynivalenol (DON)PCb Probability of DON presence or absence in barley Beta (n = 106, s = 75) FractionPCw Probability of DON presence or absence in wheat Beta (n = 201, s = 198) FractionPCmz Probability of DON presence or absence in maize Beta (n = 41, s = 32) FractionLCb Level of DON contamination in barley Cumulative (see text) ug/kgLCw Level of DON contamination in wheat Cumulative (see text) ug/kgmnLCmz Mean Level of DON contamination in maize Triangular (100, 400, 630)LCmz Level of DON contamination in maize Triangular (3, mnLCmz, 3700) ug/kgAVC Average DON concentration in feed (Ib � LCb) + (Iw � LCw) + (Imz � LCmz) ug/kgCOC DON carry over rate to milk Fixed value PercentageCMC Concentration of DON in milk AVC � COC ug/kg

D = Fumonisin B1 (FB1)PDb Probability of FB1 presence or absence in barley Fixed value FractionPDw Probability of FB1 presence or absence in wheat Fixed value FractionPDmz Probability of FB1 presence or absence in maize Beta (n = 139, s = 139) FractionLDb Level of FB1 contamination in barley Fixed value ug/kgLDw Level of FB1 contamination in wheat Fixed value ug/kgLRPDmz Level range probability maize (lg/kg) Discrete (L1, L2, L3, L4, L5; P1, P2, P3, P4, P5)

P1 probabiliy of level range L1 (0–10) Beta (n = 139, s = 0) FractionP2 probabiliy of level range L2 (10–100) Beta (n = 139, s = 30) FractionP3 probabiliy of level range L3 (101–500) Beta (n = 139, s = 42) FractionP4 probabiliy of level range L4 (501–1000) Beta (n = 139, s = 28) FractionP5 probabiliy of level range L5 (1001–5000) Beta (n = 139, s = 39) Fraction

LDmz Level of FB1 contamination in maize Uniform (min = range min, max = range max � from LRP) ug/kgAVD Average FB1concentration in feed (Ib � LDb) + (Iw � LDw) + (Imz � LDmz) ug/kgCOD FB1 carry over rate to milk Fixed value PercentageCMD Concentration of FB1 in milk AVD � COD ug/kg

E = Zearalenone (ZEN)minPEb Minimum probability of ZEN presence or absence in barley Beta (n = 34, s = 4)PEb Probability of ZEN presence or absence in barley Uniform (min = minPEb, max = 1) FractionPEw Probability of ZEN presence or absence in wheat Beta (n = 317, s = 164) FractionPEmz Probability of ZEN presence or absence in maize Beta (n = 139, s = 135) FractionmnLEb Uncertainty about the mean level of ZEN contamination in barley Triangular (1, 3, 9) ug/kgLEb Level of ZEN contamination in barley Triangular (1, mnLEb, 21) ug/kgLEw Level of ZEN contamination in wheat Cumulative, based on data ug/kgLRPEmz Level range probability maize (lg/kg) Discrete (L1, L2, L3, L4, L5; P1, P2, P3, P4, P5)

P1 probabiliy of level range L1 (0–4) Beta (n = 139, s = 4) FractionP2 probabiliy of level range L2 (4–200) Beta (n = 139, s = 13) FractionP3 probabiliy of level range L3 (21–100) Beta (n = 139, s = 63) FractionP4 probabiliy of level range L4 (101–500) Beta (n = 139, s = 58) FractionP5 probabiliy of level range L5 (>500) Beta (n = 139, s = 1) Fraction

LEmz Level of ZEN contamination in maize Uniform (min = range min, max = range max � from LRP) ug/kgAVE Average ZEN concentration in feed (Ib � LEb) + (Iw � LEw) + (Imz � LEmz) ug/kgCOE ZEN carry over rate to milk Cumulative, based on data PercentageCME Concentration of ZEN in milk AVE � COE ug/kg

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modelled this uncertainty for the level of Deoxynivalenol in wheat(LCw), using the concentrations of Deoxynivalenol recorded in eachsample.

Data from Yiannikouris and Jouany (2002) looked at the naturaldistribution of Fusarium mycotoxins in maize in France during theyears 1996 and 1997. In total there was 41 samples with 79.3%(�32 samples) proving positive for traces of Deoxynivalenol. Theprobability of Deoxynivalenol occurring (PCmz) was modelled usinga beta distribution (n = 41, s = 32). A mean concentration of 400 lg/kg was reported for 17 of the samples and 100 lg/kg for theremaining 24 samples. Other data by Veldman, Borggreve, Mul-ders, and van de Lagemaat (1992) found a mean level of 630 lg/kg for Deoxynivalenol in maize in line with published data. Theworldwide contamination of maize by Deoxynivalenol ranges from3 to 3700 lg/kg according to the Code Alimentarious Commission(CAC, 2002). To model the level of Deoxynivalenol in maize (LCmz),

a triangular distribution with a minimum equal to 3 lg/kg, a max-imum equal to 3700 lg/kg as suggested by the CAC (2002) anduncertainty about the mean (mnLCmz) was modelled using a trian-gular distribution (min = 100 lg/kg, mode = 400 lg/kg,max = 630 lg/kg) in line with published data (inputs and dataare given in Table 2). A triangular density distribution is used asa modelling tool where the range and most likely value within arange can be estimated. The triangular distribution offers consider-able flexibility in its shape while accounting for the uncertaintywithin the given range (Vose, 2000).

2.1.1.4. Fumonisin B1. From the literature reviewed, it was con-cluded that the only real risk from Fumonisin B1 in bovine feeds

Table 2 (continued)

Symbol Description Distribution Units

F = T-2 toxin (T-2)PFb Probability of T-2 presence or absence in barley Beta (n = 70, s = 21) FractionPFw Probability of T-2 presence or absence in wheat Beta (n = 921, s = 321) FractionPFmz Probability of T-2 presence or absence in maize Beta (n = 57, s = 53) FractionLFb Level of T-2 contamination in barley Lognormal (12, 15) ug/kgLFw Level of T-2 contamination in Wheat Exponential (16, 137) ug/kgLFmz Level of T-2 contamination in Maize Uniform (min = range min, max = range max) ug/kgAVF Average T-2 concentration in feed (Ib � LFb) + (Iw � LFw) + (Imz � LFmz) ug/kgCOF T-2 carry over rate to milk Cumulative, based on data PercentageCMF Concentration of T-2 in milk AVF � COF ug/kg

Mycotoxinpresent

Boolean flag

Range selection(L1,L 2 ,L3 ,L4 ,L5)

Level withinrange

Mycotoxin notpresent

Fig. 2. Sequence of events to model the level of AFB1 in maize.

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100 120 140

Concentration (µg/kg)

Cu

mu

lati

ve p

rob

abili

ty

Fig. 3. Concentration of Ochratoxin A in barley (derived from data by Prickett et al.,1999).

0

0.2

0.4

0.6

0.8

1

0 50 100 150 200 250 300 350 400

Concentration (µg/kg)

Cu

mu

lati

ve p

rob

abili

ty

Fig. 4. Concentration of deoxynivalenol in barley (derived from data by Prickettet al., 1999).

Table 3Concentrations of ochratoxin A and deoxynivalenol found in wheat samples (data byPrickett et al., 1999)

Mycotoxin Range (ug/kg) No. of samples

Ochratoxin A0–10 27

10–20 320–30 030–40 040–50 1

>50 1

Deoxynivalenol0–50 49

50–100 97100–150 34150–250 13250–450 4450–600 1

>600 0

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is from maize and maize products. This was due to the fact that theoccurrence of Fumonisin B1 in barley and wheat was rare and rep-resented a negligible risk.

Data from the MAFF survey (1999) was used to assess the risk ofFumonisin B1 occurring in maize. From the 139 samples examined,all contained concentrations of Fumonisin B1. A beta distribution(n = 139, s = 139) was used to represent the probability of Fumon-isin B1 being present in maize (PDmz). Concentrations recordedwere high with values varying between 10 and 3406 lg/kg. Theranges of concentrations with the survey on Fumonisin B1(LRPDmz) were 0–10 lg/kg (0 samples), 10–100 lg/kg (30 samples),101–500 lg/kg (42 samples), 501–1000 lg/kg (28 samples) and1001–5000 lg/kg (39 samples). The same procedure as used forAflatoxin B1 and Ochratoxin A was used to initially assign levelranges and then individual levels of Fumonisin B1 to maize sam-ples (LDmz) (inputs and data are given in Table 2).

2.1.1.5. Zearalenone. Zearalenone in barley was surveyed in the UKby Tanaka, Hasegawa, Matsuki, Lee, and Ueno (1986). Four out of31 samples (13%) contained traces of Zearalenone with the meanconcentration being 1 lg/kg. A beta distribution (n = 34, s = 4)was used to represent this uncertainty surrounding the minimumprobability of Zearalenone being present or absent in these sam-ples (minPEb). The probability of the presence of Zearalenone inbarley (PEb) was subsequently modelled using a uniform distribu-tion (minimum = minPEb, maximum = 1). Additional surveys on10 samples in Scotland revealed that 100% of samples were con-taminated with Zearalenone. The mean value recorded was 9 lg/kg. A study by the Home Grown Cereals Authority UK (HGCA) in2004 revealed a mean concentration of 3 lg/kg, a maximum sur-veyed value of 21 lg/kg and a 95th percentile of 10 lg/kg. The le-vel of Zearalenone in barley (LEb) was represented by a triangulardistribution with a minimum value of 1 lg/kg (Tanaka et al.,1986) and a maximum value equal to that as reported by the HGCA(2004) (21 lg/kg). Uncertainty about the mean level of Zearale-none in barley (mnLEb) was modelled using a triangular distribu-tion with the minimum and maximum values corresponding tothose given by Tanaka et al. (1986) (i.e. minimum = 1 lg/kg, max-imum = 9 lg/kg) and the mean equal to 3 lg/kg as given in surveywork by the HGCA (2004). Subsequently the standard deviationwas adjusted until the 95th percentile recorded by the HGCA(2004) (i.e. 10 lg/kg) was reached.

The occurrence of Zearalenone in wheat has been studied byVrabcheva, Gessler, Usleber, and Martlbauer (1996). 140 samplesof Bulgarian wheat were tested post harvest for Zearalenone witha contamination frequency of 69%. The average level of contamina-tion in positive samples was calculated at 17 lg/kg with a maxi-mum recorded value of 120 lg/kg. A similar study of Russianwheat for Zearalenone (Tutelyan, 2004) found 41 out of 60 samplescontaminated in a survey carried out in 1992 and 13 out of 56 po-sitive results for the year 1997. The mean concentration of Zearal-enone found in the wheat was 180 lg/kg and 10 lg/kg,respectively, for each survey. Wheat samples for the years 1996and 1997 in France were both reported to be 12% positive for Zea-ralenone (46 samples and 69 samples, respectively) (Yiannikouris& Jouany, 2002). The mean concentration in wheat was 9 lg/kgin 1996 and 7 lg/kg in 1997. Studies by the HGCA of wheat inthe UK estimated mean concentrations of 10.6 lg/kg, 7 lg/kg and65 lg/kg for the years 2002, 2003 and 2004, respectively. The sam-pling data by Vrabcheva et al. (1996), Tutelyan (2004) andYiannikouris and Jouany (2002) was combined giving a total num-ber of samples equal to 370 with 164 (44%) of these being positivefor Zearalenone. This uncertainty in the probability of Zearalenonebeing present in wheat (PEw) was accounted for using a beta distri-bution (n = 370, s = 164). Uncertainty about the mean concentra-tion of Zearalenone in wheat was given by a cumulative

distribution based on data (Fig. 5). A 95th percentile equal to304 lg/kg was estimated for the year 2004 in a study by the HGCA.Uncertainty about the standard deviation was modelled by meansof a back calculation. This was achieved by modelling the 95th per-centile with a uniform distribution (minimum = cumulative basedon mean values, maximum = 304; in line with published data). Astandard deviation was thus selected to give the required 95th per-centile value (304 lg/kg) for the distribution. The variability in thelevel of Zearalenone in wheat (LEw) was then modelled using a lognormal distribution with the mean equal to that in the cumulativedistribution and the standard deviation corresponding to the valueestimated previous.

The MAFF survey of mycotoxins in maize imported into the UKin 1999 revealed that 135 out of the 139 samples taken were posi-tive for Zearalenone. A beta distribution (n = 139, s = 135) was usedto represent the probability of Zearalenone being present in maize(PEmz). The ranges of concentrations with the survey on Zearale-none (LRPEmz) were 0–4 lg/kg (4 samples), 4–20 lg/kg (13 sam-ples), 21–100 lg/kg (63 samples), 101–500 lg/kg (58 samples)and >500 lg/kg (1 sample = 584 lg/kg). The same procedure asused for Aflatoxin B1, Ochratoxin A and Fumonisin B1 was usedto assign level ranges and individual levels of Zearalenone to maizesamples. These figures (normalised probability and individual con-centrations) were then used in a discrete distribution giving con-centrations for Zearalenone in maize (LEmz). Inputs and data aregiven in Table 2.

2.1.1.6. T-2 toxin. Seventy samples of barley were collected in theUK in 2004 and tested for the presence of T-2 toxin (HGCA,2004). Seventy percent of the samples (49) were less than the limitof quantification (<10 lg/kg) with 30% (21) proving to have T-2contamination. This uncertainty in the probability of a positivesample of T-2 in barley (PFb) was modelled using a beta distribu-tion (n = 70, s = 21). The average concentration of T-toxin in thebarley was 12 lg/kg, the 95th percentile was 37 lg/kg and themean was 12 lg/kg. To account for this uncertainty, the level ofT-2 in barley (LFb) was modelled using a normal distribution witha mean of 12 lg/kg and a standard deviation calculated such thatthe 95th percentile corresponded to 37 lg/kg as suggested by theHGCA (2004).

Another survey by the HGCA (2004) examined T-2 contamina-tion of wheat for the years 2002, 2003 and 2004. 921 samples werecollected over the three years harvest period with 321 of the sam-ples containing T-2 toxin contamination. The uncertainty in theprobability of T-2 occurring in wheat samples (PFw) was based onthis data using a beta distribution (n = 921, s = 321). For the years

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70

Concentration (µg/kg)

Cu

mu

lati

ve p

rob

abili

ty

Fig. 5. Mean concentrations of Zearalenone in wheat (derived from data by HGCA,2004; Tutelyan, 2004; Vrabcheva et al., 1996; Yiannikouris and Jouany, 2002).

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2002 and 2004 the mean concentration found was less than 20 lg/kg (where the mean was found to be less than 20 lg/kg, a value of10 lg/kg was used) and in 2004 the mean value was 22 lg/kg. Themaximum values for each year were 75 lg/kg and 199 lg/kg.Uncertainty about the mean levels was modelled with a uniformdistribution (minimum = 10 lg/kg, maximum = 22 lg/kg), whilethe uncertainty about the maximum level was also modelled usinga uniform distribution (minimum = 75 lg/kg, maximum = 199 lg/kg). The level of T-2 in wheat (LFw) was subsequently modelledusing an exponential distribution with the mean and maximumlevels corresponding to the previously modelled uniform distribu-tions for each parameter, respectively.

In relation to T-2 toxin in maize, its occurrence was looked at incountries in the EU in the SCOOP (Reports on tasks for ScientificCooperation) task report 3.2.10 – Part A trichothecenes (2001). InFrance, one survey of 17 samples resulted in 13 positive contami-nations. A second survey of 40 samples found that all the sampleshad T-2 present in them. When combined there was a total numberof samples equal to 57 with 53 positives. This uncertainty wasmodelled using a beta distribution (n = 57, s = 53) to account forthe probability of T-2 occurring in maize (PFmz). All of the 53 posi-tive samples were within the range 10–29.9 lg/kg. The other 4samples were below the limit of detection (<10 lg/kg) and takenas 0 lg/kg. The normalised probability for each range (<10 lg/kgand 10–29.9 lg/kg) was subsequently calculated along with theuncertainty in the level of T-2 contamination for each range (mod-elled using a uniform distribution similar to that used for AflatoxinB1, Ochratoxin A, Fumonisin B1 and Zearalenone). The uncertaintyin the level of T-2 in maize (LFmz) was then modelled using a dis-crete distribution. The inputs and data are summarised in Table 2.

2.1.2. Cereal inclusion rates in feedIn order to gain a comprehensive insight into the inclusion rates

of barley, wheat and maize/maize products in dairy cow concen-trate feeds, various feed formulations used within Ireland wereexamined and assessed. This included feeding studies conductedby Teagasc (2001) on sample dairy cow concentrate rations, max-imum inclusion levels of feed components in concentrate mixesand concentrate feeding to balance grass/silage based diets. Alsocontact was made with GAIN feeds (the animal feed manufacturingsector of Glanbia) to obtain information on their feed ingredientusage by percentage for dairy cow consumption. Overall it wasnoted that feeding formulations change regularly depending oningredient availability and pricing. Model and distribution inputsfor feed inclusion rates are displayed in Table 4. After assessingall collected data it was decided on the following as fixed inclusionrates for dairy cow concentrate feeding:

Barley: 28% (Ib)Wheat: 7% (Iw)Maize and maize products: 41% (Im)

2.1.3. Mycotoxin carry over ratesThe extent to which AFM1 is carried over to milk can vary

greatly. Jones et al. (1994) reported the carry over rate to be circa1.7% while studies by the European Food Safety Authority (EFSA) in2004 suggested a mean carry over rate of 2% increasing to 6% forhigh yielding cows. Henry et al. (2004) suggested ranges from0.2% to 4%. More recent studies estimate a wider carry over rangefrom 0.3% to 6.2% (Henry et al., 2004). An approximate carry over of0.1% was suggested by Blüthgen et al. (2004). To account for theuncertainty in the percentage carry over of AFM1 to milk (COA),the individual rates (Table 5) described above were fitted to anexponential distribution.

Due to the fact that Ochratoxin A is degraded by rumen micro-flora in bovines, it has been suggested that the carry over rate tomilk is minimal. Few studies have been carried out in this area.However, a study by Galtier (1998) calculated that if a cow wasfed an oral dose of 1 g/day, it would result in 100 lg/kg of Ochra-toxin A in milk. This results in a carry over rate of 0.01% and wastaken as a fixed value to represent Ochratoxin A carry over to milk(COB) in the model.

Similarly with Deoxynivalenol, transfer to bovine milk is esti-mated to be small; however, there is very little research in the area.A carry over of 0.22% was calculated from figures of dose andresulting concentration in milk (Galtier, 1998). Again this (COC)was taken as a fixed value in the model.

For the carry over of Fumonisin B1 to milk (COD), two valueswere identified. The first was a value of 0.11% reported by the EFSA(2005). A transfer rate of 0.05% was reported as the average carryover rate for a single administration of 3 mg of toxin per kg of feedby Yiannikouris and Jouany (2002). A uniform distribution (mini-mum = 0.05, maximum = 0.11) was used to account for thisuncertainty.

Transfer of Zearalenone to milk has revealed varying carry overrates. Yiannikouris and Jouany (2002) reported transfer rates of0.06%, 0.016% and 0.008%, depending on the dose of the toxinadministered. Rates of 0.00625% and 1.924% were estimated fromother feeding studies (Galtier, 1998). A cumulative distributionwas used to model the uncertainty surrounding Zearalenone carry

Table 4Model and distribution inputs for feed inclusion rates

Symbol Description Meanvalue

Distribution Units

Feed ingredient inclusion ratesIb Inclusion rate of barley in

feed0.28 From data, see

textPercentage

Iw Inclusion rate of wheat infeed

0.07 From data, seetext

Percentage

Io Inclusion rate of otheringredients

0.41 From data, seetext

Percentage

In feed From data, seetext

Percentage

Imz Inclusion rate of maize infeed

0.24 From data, seetext

Percentage

Table 5Percentage carry over rates of AFM1, ZEN and T2 to bovine milk

Mycotoxin Carry over (%) Probability f(x) Reference

AFM10.1 Blüthgen et al. (2004)0.2 Henry et al. (2004)0.3 Henry et al. (2004)0.3 Yiannikouris and Jouany (2002)0.3 Creppy (2002)1.7 Jones et al. (1994)2 EFSA (2004)2.2 Yiannikouris and Jouany (2002)4 Henry et al. (2004)6 EFSA (2004)

ZEN0 (Best guess min)0.00625 0.2 Galtier (1998)0.008 0.4 Yiannikouris and Jouany (2002)0.016 0.6 Yiannikouris and Jouany (2002)1.924 0.8 Galtier (1998)2 (Best guess max)

T20.01 (Best guess min)0.02 0.2 Galtier (1998)0.05 0.4 Yiannikouris and Jouany (2002)0.32 0.6 Galtier (1998)2.00 0.8 Yiannikouris and Jouany (2002)2.50 (Best guess max)

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over to milk (COE) using the transfer rates reported by Yiannikourisand Jouany (2002) and Galtier (1998) and is given in Table 5.

The presence of T-2 residues in cows milk was reported to havebeen found in the range of 0.05–2% (Yiannikouris & Jouany, 2002).Research by Galtier (1998) found a carry over rate in milk of be-tween 0.02 and 0.32% for dairy cows fed 50,000 lg/kg of bodyweight. The variability in the transfer of T-2 toxin to milk (COF)was modelled using a cumulative distribution fitted to the carryover data suggested in the literature (given in Table 5).

2.1.4. Consumption dataData on the quantities of milk consumed by Irish consumers

was obtained from the Irish Universities Nutritional Alliance Sur-vey (IUNA, 2001). The mean consumption of wholemilk (WMm),low fat skimmed (LFSm) and processed milks (PMm) for adult males(18–64 years) was 195 g/day, 80 g/day and 5 g/day, respectively.The standard deviations were 220 g/day, 149 g/day and 32 g/day,respectively. To account for the variability in wholemilk, low fatskimmed and processed milk consumption, a lognormal distribu-tion was used with the mean and standard deviation for each prod-uct from the IUNA survey (2001). For adult females, averageconsumption of wholemilk (WMf) was equal to 110 g/day, lowfat skimmed (LFSf) was equal to 95 g/day and processed milk(PMf) was estimated at 5 g/day. Standard deviations were 141 g/day, 132 g/day and 31 g/day, respectively. Similar to male con-sumption, a lognormal distribution using the given mean and stan-dard deviation for female consumption of each product was usedto account for the uncertainty in consumption levels for females.The consumption data used in the model is given in Table 6.

2.1.5. Product exposure assessmentThe product exposure assessment provides an estimate of how

likely it is for an individual to be exposed to mycotoxin residues

and in what quantities they are likely to be ingested. In order tocalculate the human exposure to a mycotoxin from wholemilk,low fat skimmed milk or processed milks, firstly the concentrationof the mycotoxin in milk was calculated. This is estimated usingthe equation

ðCMx=1000Þ � P=M

where CMx is the concentration of mycotoxin in milk (ug/kg) (sub-script x = mycotoxin A, B, C, D, E or F), P is the milk product con-sumption; male or female (g/day) (wholemilk/low fat skimmedmilk/processed milk), M is the mass of individual (kg): male or fe-male, assumed to be 82.9 kg for males and 67.5 kg for females as gi-ven in IUNA study (2001).

The model equations are summarised in Table 7. Differentexposure levels for male and female were identified due todifferences in consumption data of individual products andaverage body weights for genders. A total exposure (TE) for male/females to each toxin was calculated by adding the exposure lev-els for wholemilk, low fat skimmed milk and processed milktogether.

2.1.6. Model assumptionsSimulation models frequently need to use necessary subjective

assumptions. Such assumptions can have an impact on the resultsobtained in risk and exposure assessments. Consequently, assump-tions made must be taken in context when considering model out-puts. The following modelling assumptions have been made in thedevelopment of this exposure assessment:

(i) Any mycotoxin contamination levels found in grains whichwere below the sampling limit of detection were assumedto be equal to 0. This may not be the case due to censoreddata; however, there is limited data to quantify this value.It is perceived that contamination values below the limitof detection would have little or no effect on overall humanexposure levels.

(ii) Feed production processes have little or no effect on initialmycotoxin concentrations in grain.

(iii) Feed is not pelleted and is fed in ration form.(iv) Due to the fact that comprehensive data on mycotoxins in

feed grains was unavailable for Ireland, data from othercountries is representative of what may occur in Ireland.The authors acknowledge that this may not be the case inall circumstances but believe pessimistic values have beenused therefore representing the upper end of risk.

(v) Dairy cows are fed a fixed feed formulation.(vi) Milk production processes (such as pasteurisation) have no

effect on mycotoxin concentration in milk. This is due tothe fact that the majority of mycotoxins are heat stable (Bata& Lásztity, 1999).

Table 6Model distributions and inputs for consumption data (based on IUNA, 2001)

Symbol Description Meanvalue

Distribution:lognormal

Units

Consumption dataWMm Wholemilk consumption (males) 195 Standard

deviation = 220g/day

WMf Wholemilk consumption(females)

110 Standarddeviation = 141

g/day

LFSm Low fat skimmed milkconsumption (males)

80 Standarddeviation = 149

g/day

LFSf Low fat skimmed milkconsumption (females)

95 Standarddeviation = 132

g/day

PMm Processed milk consumption(males)

5 Standarddeviation = 32

g/day

PMf Processed milk consumption(females)

5 Standarddeviation = 31

g/day

Table 7Model calculations for exposure assessment

Symbol Description Mean value Distribution Units

Exposure assessmentMm Males mean weight (18–64 yrs) 82.9 Fixed value kgEWMm,x Exposure to mycotoxin x from whole milk for males ðCMx=1000Þ �WMm=Mm

a g/kg bw/dayELFSm,x Exposure to mycotoxin x from low fat, skimmed milk for males (CMx/1000) � LFSm/Mm g/kg bw/dayEPMm,x Exposure to mycotoxin x from processed skimmed for males (CMx/1000) � PSm/Mm g/kg bw/dayTEm,x Total exposure from mycotoxin x (males) EWMm + ELFSm + EPMm g/kg bw/dayMf Females mean weight (18–64 yrs) 67.5 Fixed value kgEWMf,x Exposure to mycotoxin x from whole milk for females (CMx/1000) �WMf/Mf g/kg bw/dayELFSf,x Exposure to mycotoxin x from low fat, skimmed milk for females (CMx/1000) � LFSf/Mf g/kg bw/dayEPMf,x Exposure to mycotoxin x from processed skimmed for females (CMx/1000) � PSf/Mf g/kg bw/dayTEf,x Total exposure from mycotoxin x (females) EWMf + ELFSf + EPMf g/kg bw/day

a Where subscript x, x is equal to mycotoxin A, B, C, D, E or F (bw = body weight).

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(vii) Mycotoxins were assumed to be uniformly distributedthroughout the milk.

2.2. Model simulation

The exposure model was developed using Monte Carlo simula-tion techniques and probability distributions to account for modeluncertainty and variability. The @RISK software package, version4.0 (Palisade, USA), in combination with Microsoft Excel 2000(Microsoft, USA) was used to run the simulation. The simulationwas run for 10,000 iterations and reflects the inherent uncertaintyin the production of bovine feed, in milk consumption and in theuncertainty of the mathematical process. The probability of a toxinin milk, the level of the toxin in milk and the probability of humanexposure were outputs of the mathematical model. Monte Carlosimulation was also used to perform a sensitivity analysis of themodel to assist in the identification of critical points in the process.

3. Results and discussion

For each toxin, the risk assessment model produced:

� A probability density distribution representing the potentiallevel of bovine milk contamination with mycotoxin x (CMx;where x is equal to mycotoxin A, B, C, D, E or F). This representsthe uncertainty about the true mean value. A summary of thesimulation results, including uncertainty analysis and compari-son with EU regulations for each analysed mycotoxin, is givenin Table 8.

� A probability-exposure distribution for males and females inrelation to milk consumption (EWMf/m,x). Table 9 displays asummary of these results together with existing legislation inthe EU.

� A sensitivity analysis was subsequently conducted to provide ameasure of the most important factors affecting the risk tohuman health from an individual mycotoxin in milk. This infor-

mation was displayed on a bar chart and can be seen in Fig. 6.The sensitivity analysis may be the most important result ofthe risk assessment. It can be used to identify factors for whichrisk management strategies can be based in order to reduce theoverall exposure to mycotoxins. Rank order correlation deter-mines the correlation between input variables and outputs.The correlation coefficient lies between �1 (direct negative cor-relation) and +1 (direct positive correlation). Correlation valuesin the vicinity of zero indicate a weak predictive value of the var-iable (Cassin, Lammerding, Todd, Ross, & McColl, 1998).

Mean levels for the mycotoxins assessed in milk all fell belowEU limits (Table 8). Aflatoxin M1 was singled out as the toxin ofgreatest concern. In certain circumstances its concentration ex-ceeded the EU limit of 0.05 lg/kg in milk. This data was in line withthe literature reviewed which also found Aflatoxin M1 occurring atpotentially high levels in milk (EFSA, 2004). However, it should benoted that for all other mycotoxins assessed in this study, EU limitsonly exist for their presence in cereal based foods and are thus notstrictly comparable to this risk model which focused on milk. It hasnot been possible to estimate a tolerable daily intake (TDI) for Afla-toxin which is a carcinogen. The estimated TDI’s for all other myco-toxins by the exposure assessment model were below thoseestimated by the EU indicating negligible risk to humans. It is sug-gested that the regulatory limits for Aflatoxin in milk are set to en-sure that any risk from total dietary intake is very low.Occasionally eating foods containing Aflatoxin at levels marginallyabove the regulatory limit will not increase that very low risk (UKfood standards agency, 2005). On examination of the sensitivity re-sults for all the assessed mycotoxins, it was clear that in most casesrisk estimates were very sensitive to the initial concentration ofeach toxin in maize followed by the level in barley and wheat. Thisconcurs with the literature reviewed. The sensitivity analysis alsosingles out the concentration of toxins in maize as the area whichneeds most attention in relation to crop management and agricul-tural practice. The carry over rate also warrants furtherinvestigation.

Table 8Simulated uncertainty distribution about individual mycotoxin levels in milk

AFM1(lg/kg) OTA (lg/kg) DON (lg/kg) FB1 (lg/kg) ZEN (lg/kg) T-2 (lg/kg)

Mean 0.0161 0.0002 1.0000 0.3600 0.3900 0.07225th Percentile 0.0002 8.84 � 10�7 0.0049 0.0093 0.0002 0.000695th Percentile 0.0834 0.0009 4.0047 1.4391 2.5570 0.2881Limit 0.05a 3b 500c 400d 50c e

a EU limit for AFM1 in milk (Commission Regulation 2003/2174/EC).b EU limit for cereal based food (Commission Regulation (EC) 472/2002).c EU limits for cereal based foods (no limits exist for milk) (Commission Regulation (EC) No. 856/2005).d EU limits for maize based products (no limits exist for milk) (Commission Regulation (EC) No. 856/2005).e No limit for the presence of T-2 in milk/food products exists (Commission Regulation (EC) No. 856/2005).

Table 9Simulated exposure to individual mycotoxins from milk for males and females

AFM (pg/kg bw/day)

OTA (pg/kg bw/day)

DON (pg/kg bw/day) FB1 (pg/kg bw/day) ZEN (pg/kg bw/day) T-2 (pg/kg bw/day)

Male Female Male Female Male Female Male Female Male Female Male Female

Mean 8.619 9.371 0.046 0.049 608.520 650.738 327.097 351.125 43.558 46.827 32.492 34.9925th Percentile 0.356 0.381 0.002 0.002 9.961 10.293 16.960 18.253 0.478 0.479 0.927 0.98895th Percentile 212.559 238.037 2.353 2.426 13580.226 13833.657 4650.418 5021.688 6553.209 6895.027 973.727 1168.956

Tolerable daily intake (pg/kg bw/day)

–a –a 5000b 5000b 1,000,000b 1,000,000b 2,000,000b 2,000,000b 200,000b 200,000b 60,000b 60,000b

a No tolerable daily intake has been estimated (see text) (UK food standard agency, 2004).b Tolerable daily intake estimated by the EU (Commission Regulation (EC) No. 856/2005).

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4. Conclusion

As natural and unavoidable contaminants of important agricul-tural commodities, mycotoxins have continued to severely impactanimal health and consequently human health, which may haveimplications for livestock production, crops production and theeconomy. The risk is well recognised, but at present it has not beenquantified accurately. Exposure modelling and risk assessment canbe valuable tools in assessing risks to humans and animal frommycotoxins in the feed/food chain. The quantitative exposureassessment developed in this study tries to address the deficiencyin scientific literature on the estimation of risk from feedbornehazards (Hinton, 2000; Notermans, 2003) and serves as an initialattempt to link the animal feed chain and the human food chain.The model assesses the potential human exposure to six mycotox-ins in dairy milk. There has been no published attempt in scientificliterature to simultaneously assess human exposure to thesemycotoxins from milk consumption. Results fit well with observeddata, suggesting that the mathematical approximations of all reallife variables are justified. Except for Aflatoxin M1, the simulatedexposure levels for mycotoxins in milk are below the limits setby the EU. Under certain conditions Aflatoxin M1 exceeded EU lim-its. A sensitivity analysis also suggests that the key to reducingmycotoxin contamination is at the field level prior to the harvest-ing of grain for feed production. Results from the exposure assess-ment model suggested that the presence of mycotoxins in bovinefeed at normal contamination levels should not give rise to signif-icant mycotoxin concentrations in milk. Evidence suggests thatmycotoxins may never be completely removed from the feed-to-food chain but that current exposure levels are likely to be smallin dairy milk and well below EU guidelines. It can be concludedthat, from a risk perspective, the presence of mycotoxins in bovinemilk poses little risk to man. If more accurate data becomes avail-able on the biological fate and carry over of mycotoxins to bovine

food products, the developed model should be updated and devel-oped further. The model identified data gaps in these areas whiledirecting future research efforts to fill these gaps.

References

Bata, Á., & Lásztity, R. (1999). Detoxification of mycotoxin-contaminated foodand feed by microorganisms. Trends in Food Science and Technology, 10,223–228.

Blüthgen, A., Hammer, P., & Teufel, P. (2004). Mycotoxins in milk production.Occurrence, relevance and possible minimization in the production chain feeds-milk. Kieler Milchwirtschaftliche Forschungsberichte, 56(4), 219–263.

Codex Alimentarius Commission (2002). Discussion paper on deoxynivalenol, USAwheat and barley scab initiative. Available from http://www.scabusa.org/pdfs/02-03_CODEX_DON.pdf Accessed 13/11/04 3.30 pm.

Cassin, M. H., Lammerding, A. M., Todd, E. C. D., Ross, W., & McColl, S. (1998).Quantitative risk assessment of Escherichia coli O157:H7 in ground beef burgers.International Journal of Food Microbiology, 41, 21–45.

Creppy, E. (2002). Update of survey, regulation and toxic effects of mycotoxins inEurope. Toxicology Letters, 127, 19–28.

D’Mello, J. P. F., Placinta, C. M., & Macdonald, A. M. C. (1999). A review of worldwidecontamination of cereal grains and animal feed with Fusarium mycotoxins.Journal of Animal Feed Science and Technology, 78(1–2), 21–37.

EFSA (2004). Opinion of the scientific panel on contaminants in food chain on arequest from the commission related to aflatoxin B1 as undesirable substance inanimal feed. The European Food Safety Authority Journal, 39, 1–27.

EFSA (2005). Opinion of the scientific panel on contaminants in food chain on arequest from the commission related to fumonisins as undesirable substance inanimal feed. The European Food Safety Authority Journal, 235, 1–32.

Galtier, P. (1998). Biological fate of mycotoxins in animals. Revue du MédicineVéterenaire, 149, 549–554.

Home Grown Cereals Authority (HGCA) (2004). Investigation of fusariummycotoxins in UK barley and oat production. Available from http://www.hgca.com/content.template/4/0/About%20HGCA/About%20HGCA/About%20HGCA%20Home%20Page.mspx.

International Agency for Research on Cancer (1987). Some naturally occurringsubstances: food items and constituents, heterocyclic aromatic amines andmycotoxins (Vol. 56, pp. 245–489). Available from http://www-cie.iarc.fr/htdocs/monographs/vol56/09-afl.htm.

IUNA (2001). Irish Universities Nutritional Alliance North/South Ireland FoodConsumption Survey. Report Available from Food Safety Authority, Dublin,Ireland.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

RANK CORRELATION

AFM1 410.010.0550.0761.0109.0

ZEN 0.412 0.806 0.039 0.013 0.21 0.115 0.022

OTA 150.0620.094.0926.0844.0

FB1 220.0891.0933.0341.0688.0

DON 20.061.0982.0310.0650.0876.0

T-2 0.214 0.881 0.103 0.065 0.264 0.133 0.005

Concentrationin maize

Carry overrate

Concentraionin barley

Concentrationin wheat

Whole milkconsumption

Low fatskimmed milkconsumption

Processedmilk

consumption

Fig. 6. Sensitivity analysis for human exposure to mycotoxins in bovine milk.

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Henry, S. H., Whitaker, T., Rabbani, I., Bowers, J., Park, D., Price, W., et al. (2004).Aflatoxin M1. Available From http://www.inchem.org/documents/jecfa/jecmono/v47je02.htm Accessed 16/02/06.

Hinton, M. H. (2000). Infections and intoxications associated with animal feed andforage which may present a hazard to human health. The Veterinary Journal, 159,124–138.

Jones, F. T., Genter, M. B., Hagler, W. M., Hansen, J. A., Mowrey, B. A., Poore, M. H.,et al. (1994). Understanding and coping with the effects of mycotoxins inlivestock feed and forage. Available from http://www.ces.ncsu.edu/disaster/drought/dro-29.html. Accessed 12/12/05.

Knutti, R., & Schlatter, C. (1982). Distribution of aflatoxin in whole peanut kernels,sampling plans for small samples. Zeitschrift fur Lebensmittel Untersuchung andForschung, 174, 122–128.

Kuiper-Goodman, T., Scott, P. M., & Watanabe, H. (1987). Risk assessment of themycotoxin zearalenone. Regulatory Toxicology and Pharmacology, 7, 253–306.

Larsen, J. C., Hunt, J., Perrin, I., & Ruckenbauer, P. (2004). Workshop ontrichothecenes with a focus on DON: summary report. Toxicology Letters,153(1), 1–22.

MAFF (1999). Ministry for agriculture food and fisheries UK – survey for aflatoxins,ochratoxin A, fumonisins and zearalenone in raw maize. Available from http://archive.food.gov.uk/maff/archive/food/infsheet/1999/no192/192afla.htmAccessed 21/01/05.

Marquardt, R. R. (1996). Effects of molds and their toxins on livestock performance:a western Canadian perspective. Animal Feed Science and Technology, 58(1–2),77–89.

Meky, F. A., Turner, P. C., Ashcroft, A. E., Miller, J. D., Qiao, Y. L., Roth, M. J., et al.(2003). Development of a urinary biomarker of human exposure todeoxynivalenol. Food and Chemical Toxicology, 41, 265–273.

Notermans, S. (2003). Food authenticity and traceability, ensuring the safety ofanimal feed. Available from www.foodmicro.nl/ensuringsafety.pdf Accessed 23/11/05.

Petzinger, E., & Weidenbach, A. (2002). Mycotoxins in the food chain: The role ofochratoxins. Livestock Production Science, 76, 245–250.

Placinta, C. M., D’Mello, J. P. F., & Macdonald, A. M. C. (1999). A review of worldwidecontamination of cereal grains and animal feed with Fusarium mycotoxins.Animal Feed Science and Technology, 78(1–2), 21–37.

Prickett, A. J., Macdonald, S., & Wildley, K. B. (1999). Survey of mycotoxins in storedgrain from the 1999 harvest in the UK Home-Grown Cereals Authority.

Available from http://www.hgca.com/publications/documents/cropresearch/230_complete_final_report.pdf Accessed 04/10/05.

Quillien, J. F. (2002). Mycotoxins. Fevia: ‘The representative employers’ organisationfor the food industry in Belgium. Available at http://www.fevia.be/pdf/FLAIR%20FLOW%20ONE%20PAGERS/Synthese/mycotoxins.pdf Accessed 02/11/04.

SCOOP (2001). Collection of occurrence data of Fusarium toxins in food andassessment of dietary intake by the population of EU member states. Taskreport 3.2.10 – Part A trichothecenes, Report of the EU, Scientific Co-operationon Questions Relating to Food (pp. 159–164).

Tanaka, T., Hasegawa, A., Matsuki, Y., Lee, U. S., & Ueno, Y. (1986). A limited surveyof Fusarium mycotoxins nivalenol, deoxynivalenol and zearalenone in 1984 UKharvested wheat and barley. Food Additive Contamination, 3(3), 247–252.

Teagasc (2001). Fodder and feed events. Available from http://www.teagasc.ie/publications/2001/fodderandfeed/fodderandfeed2001.pdf Accessed 05/12/05.

Tritscher, A. M., & Page, S. W. (2004). The risk assessment paradigm and itsapplication for trichothecenes. Toxicology Letters, 153, 155–163.

Turner, P. C., Nikiema, P., & Wild, C. P. (1999). Fumonisin contamination of food:progress in development of biomarkers to better assess human health risks.Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 443,81–93.

Tutelyan, V. A. (2004). Deoxynivalenol in cereals in Russia. Toxicology Letters, 153(1),173–179.

United Kingdom food standard agency (2005). Survey of spices for aflatoxins andochratoxin A. Available from http://www.food.gov.uk/multimedia/pdfs/fsis7305.pdf Accessed: 19/06/05.

Veldman, A., Borggreve, G. J., Mulders, E. J., & van de Lagemaat, D. (1992).Occurrence of the mycotoxins ochratoxin A, zearalenone and deoxynivalenol infeed components. Food Additive Contamination, 9(6), 647–655.

Vose, D. (2000). Quantifying uncertainty about model parameters. In Risk analysis(2nd ed., pp. 155). New York, NY, USA: Wiley and Sons Ltd.. 200.

Vrabcheva, T., Gessler, R., Usleber, E., & Martlbauer, E. (1996). First survey on thenatural occurrence of Fusarium mycotoxins in Bulgarian wheat. Mycopathologia,136(1), 47–52.

Whitaker, T. B. (1977). Sampling granular foodstuffs for aflatoxin. Pure and AppliedChemistry, 49, 1709–1717.

Yiannikouris, A., & Jouany, J. P. (2002). Mycotoxins in feeds and their fate in animals:A review. Animal Research, 51, 81–89.

R. Coffey et al. / Food Control 20 (2009) 239–249 249